This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L6-v2 on the query-doc, anc-pos-neg and anc-pos-neg-2 datasets using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

Model Type: Cross Encoder
Base model: cross-encoder/ms-marco-MiniLM-L6-v2
Maximum Sequence Length: 512 tokens
Number of Output Labels: 1 label
Training Datasets:
- query-doc
- anc-pos-neg
- anc-pos-neg-2
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Cross Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("emilwin/reranker-ms-marco-sympathy-docs")
# Get scores for pairs of texts
pairs = [
    ['What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?', '# API\n\nADAF API\n========\n\nAPI for working with the ADAF type.\n\nImport this module like this:\n\n```\nfrom sympathy.api import adaf\n\n```\n\nThe ADAF structure\n------------------\n\nAn ADAF consists of three parts: meta data, results, and timeseries.\n\nMeta data contains information about the data in the ADAF. Stuff like when,\nwhere and how it was measured or what parameter values were used to generated\nit. A general guideline is that the meta data should be enough to (at least in\ntheory) reproduce the data in the ADAF.\n\nResults and timeseries contain the actual data. Results are always scalar\nwhereas the timeseries can have any number of values.\n\nTimeseries can come in several systems and each system can contain several\nrasters. Each raster in turn has one basis and any number of timeseries. So\nfor example an experiment where some signals are sampled at 100Hz and others\nare sampled only once per second would have (at least) two rasters. A basis\ndoesn’t have to be uniform but can have samples only every now and then.\n\n'],
    ['What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?', '# Node\n\nTable to ADAF\n=============\n\nConvert a Table into an ADAF, placing its content in the specified container.\n\nDocumentation\n-------------\n\nThe target container in the ADAF is specified in the configuration GUI. If the\ntimeseries container is chosen it is necessary to specify the column in the\nTable which will be the time basis signal in the ADAF. You can also specify\nthe name of the system and raster containers.\n\nSee also Working with ADAF for tips about how to use these conversion\nnodes.\n\n'],
    ['What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?', '# Node\n\nSelect columns in ADAF with structure Table\n===========================================\n\nSelect the columns to keep in ADAF using selection table created by ADAF structure to table\n\nDocumentation\n-------------\n\nUse this node if you’re only interested in some of the data in an ADAF\ne.g. for performance reasons.\n\nThe Table/Tables argument should have four columns, which must be named\nType, System, Raster, and Parameter. These columns hold the names of the\ncorresponding fields in the ADAF/ADAFs.\n\nDefinition\n----------\n\n### Input ports\n\n> **selection**\n> :   Type: table\n> ADAF structure selection\n> \n> **data**\n> :   Type: adaf\n> ADAF data matched with selection\n\n### Output ports\n\n> **data**\n> :   Type: adaf\n> ADAF data after selection\n\n### Configuration\n\n> **Remove selected columns** (complement)\n> :   When enabled, the selected columns will be removed. When disabled, the non\\-selected columns will be removed.\n\n### Related nodes\n\n* Select columns in ADAFs with structure Table\n* Select columns in ADAFs with structure Tables\n* ADAF structure to Table\n* Select categories in ADAFs\n\n### Examples\n\nThis node is used in the following example flows:\n\n* SelectColumns.syx\n\n### Implementation\n\n*class* node\\_select\\_adaf\\_columns.SelectColumnsADAFWithTable\\[source]\n\n'],
    ['What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?', '# Node\n\nSelect categories in ADAFs\n==========================\n\nSelect what catgories to exist in the output ADAFs.\n\nDocumentation\n-------------\n\nA selector of categories in ADAFs can be used to drop parts of ADAFs.\nThe main reason to do this is when the ADAFs contain data that is no longer\nneeded further along a workflow. Dropping the unnecessary data can then be\nused as a way to try to optimize the workflow.\n\nDefinition\n----------\n\n### Input ports\n\n> **port1**\n> :   Type: \\[adaf]\n> Input ADAFs\n\n### Output ports\n\n> **port3**\n> :   Type: \\[adaf]\n> ADAFs with selected categories\n\n### Configuration\n\n> **Select meta group** (select\\_meta)\n> :   Select the meta group for inclusion in the output.\n> \n> **Select specific rasters:** (select\\_rasters)\n> :   Select specific rasters for inclusion in the output.\n> \n> **Select result group** (select\\_res)\n> :   Select the result group for inclusion in the output.\n\n### Related nodes\n\n* ADAF to Table\n\n### Examples\n\nThis node is used in the following example flows:\n\n* SelectCategoryInADAFs.syx\n\n### Implementation\n\n*class* node\\_category\\_selector.CategorySelectorMultiple\\[source]\n\n'],
    ['In Sympathy: What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?', '# API\n\nADAF API\n========\n\nAPI for working with the ADAF type.\n\nImport this module like this:\n\n```\nfrom sympathy.api import adaf\n\n```\n\nThe ADAF structure\n------------------\n\nAn ADAF consists of three parts: meta data, results, and timeseries.\n\nMeta data contains information about the data in the ADAF. Stuff like when,\nwhere and how it was measured or what parameter values were used to generated\nit. A general guideline is that the meta data should be enough to (at least in\ntheory) reproduce the data in the ADAF.\n\nResults and timeseries contain the actual data. Results are always scalar\nwhereas the timeseries can have any number of values.\n\nTimeseries can come in several systems and each system can contain several\nrasters. Each raster in turn has one basis and any number of timeseries. So\nfor example an experiment where some signals are sampled at 100Hz and others\nare sampled only once per second would have (at least) two rasters. A basis\ndoesn’t have to be uniform but can have samples only every now and then.\n\n'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?',
    [
        '# API\n\nADAF API\n========\n\nAPI for working with the ADAF type.\n\nImport this module like this:\n\n```\nfrom sympathy.api import adaf\n\n```\n\nThe ADAF structure\n------------------\n\nAn ADAF consists of three parts: meta data, results, and timeseries.\n\nMeta data contains information about the data in the ADAF. Stuff like when,\nwhere and how it was measured or what parameter values were used to generated\nit. A general guideline is that the meta data should be enough to (at least in\ntheory) reproduce the data in the ADAF.\n\nResults and timeseries contain the actual data. Results are always scalar\nwhereas the timeseries can have any number of values.\n\nTimeseries can come in several systems and each system can contain several\nrasters. Each raster in turn has one basis and any number of timeseries. So\nfor example an experiment where some signals are sampled at 100Hz and others\nare sampled only once per second would have (at least) two rasters. A basis\ndoesn’t have to be uniform but can have samples only every now and then.\n\n',
        '# Node\n\nTable to ADAF\n=============\n\nConvert a Table into an ADAF, placing its content in the specified container.\n\nDocumentation\n-------------\n\nThe target container in the ADAF is specified in the configuration GUI. If the\ntimeseries container is chosen it is necessary to specify the column in the\nTable which will be the time basis signal in the ADAF. You can also specify\nthe name of the system and raster containers.\n\nSee also Working with ADAF for tips about how to use these conversion\nnodes.\n\n',
        '# Node\n\nSelect columns in ADAF with structure Table\n===========================================\n\nSelect the columns to keep in ADAF using selection table created by ADAF structure to table\n\nDocumentation\n-------------\n\nUse this node if you’re only interested in some of the data in an ADAF\ne.g. for performance reasons.\n\nThe Table/Tables argument should have four columns, which must be named\nType, System, Raster, and Parameter. These columns hold the names of the\ncorresponding fields in the ADAF/ADAFs.\n\nDefinition\n----------\n\n### Input ports\n\n> **selection**\n> :   Type: table\n> ADAF structure selection\n> \n> **data**\n> :   Type: adaf\n> ADAF data matched with selection\n\n### Output ports\n\n> **data**\n> :   Type: adaf\n> ADAF data after selection\n\n### Configuration\n\n> **Remove selected columns** (complement)\n> :   When enabled, the selected columns will be removed. When disabled, the non\\-selected columns will be removed.\n\n### Related nodes\n\n* Select columns in ADAFs with structure Table\n* Select columns in ADAFs with structure Tables\n* ADAF structure to Table\n* Select categories in ADAFs\n\n### Examples\n\nThis node is used in the following example flows:\n\n* SelectColumns.syx\n\n### Implementation\n\n*class* node\\_select\\_adaf\\_columns.SelectColumnsADAFWithTable\\[source]\n\n',
        '# Node\n\nSelect categories in ADAFs\n==========================\n\nSelect what catgories to exist in the output ADAFs.\n\nDocumentation\n-------------\n\nA selector of categories in ADAFs can be used to drop parts of ADAFs.\nThe main reason to do this is when the ADAFs contain data that is no longer\nneeded further along a workflow. Dropping the unnecessary data can then be\nused as a way to try to optimize the workflow.\n\nDefinition\n----------\n\n### Input ports\n\n> **port1**\n> :   Type: \\[adaf]\n> Input ADAFs\n\n### Output ports\n\n> **port3**\n> :   Type: \\[adaf]\n> ADAFs with selected categories\n\n### Configuration\n\n> **Select meta group** (select\\_meta)\n> :   Select the meta group for inclusion in the output.\n> \n> **Select specific rasters:** (select\\_rasters)\n> :   Select specific rasters for inclusion in the output.\n> \n> **Select result group** (select\\_res)\n> :   Select the result group for inclusion in the output.\n\n### Related nodes\n\n* ADAF to Table\n\n### Examples\n\nThis node is used in the following example flows:\n\n* SelectCategoryInADAFs.syx\n\n### Implementation\n\n*class* node\\_category\\_selector.CategorySelectorMultiple\\[source]\n\n',
        '# API\n\nADAF API\n========\n\nAPI for working with the ADAF type.\n\nImport this module like this:\n\n```\nfrom sympathy.api import adaf\n\n```\n\nThe ADAF structure\n------------------\n\nAn ADAF consists of three parts: meta data, results, and timeseries.\n\nMeta data contains information about the data in the ADAF. Stuff like when,\nwhere and how it was measured or what parameter values were used to generated\nit. A general guideline is that the meta data should be enough to (at least in\ntheory) reproduce the data in the ADAF.\n\nResults and timeseries contain the actual data. Results are always scalar\nwhereas the timeseries can have any number of values.\n\nTimeseries can come in several systems and each system can contain several\nrasters. Each raster in turn has one basis and any number of timeseries. So\nfor example an experiment where some signals are sampled at 100Hz and others\nare sampled only once per second would have (at least) two rasters. A basis\ndoesn’t have to be uniform but can have samples only every now and then.\n\n',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Dataset: sydoc-tester
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": false 4}

Metric	Value
map	0.3233 (+0.1907)
mrr@10	0.3233 (+0.2013)
ndcg@10	0.3488 (+0.1993)

Cross Encoder Reranking

Datasets: NanoMSMARCO_R100, NanoNFCorpus_R100 and NanoNQ_R100
Evaluated with CrossEncoderRerankingEvaluator with these parameters:
json
1{ 2 "at_k": 10, 3 "always_rerank_positives": true 4}

Metric	NanoMSMARCO_R100	NanoNFCorpus_R100	NanoNQ_R100
map	0.5604 (+0.0708)	0.3633 (+0.1023)	0.6359 (+0.2163)
mrr@10	0.5468 (+0.0693)	0.5569 (+0.0570)	0.6529 (+0.2262)
ndcg@10	0.6088 (+0.0683)	0.3953 (+0.0703)	0.6934 (+0.1928)

Cross Encoder Nano BEIR

Dataset: NanoBEIR_R100_mean
Evaluated with CrossEncoderNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nfcorpus", 5 "nq" 6 ], 7 "rerank_k": 100, 8 "at_k": 10, 9 "always_rerank_positives": true 10}

Metric	Value
map	0.5199 (+0.1298)
mrr@10	0.5855 (+0.1175)
ndcg@10	0.5658 (+0.1105)

Training Details

Training Datasets

query-doc

Dataset: query-doc
Size: 15,230 training samples
Columns: query, document, and label
Approximate statistics based on the first 1000 samples:
query document label
type string string int
details
min: 31 characters
mean: 150.61 characters
max: 242 characters
min: 163 characters
mean: 1891.69 characters
max: 12851 characters
0: ~74.10%
1: ~25.90%

Samples:

query	document	label
`What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?`	# API[object Object][object Object]ADAF API[object Object]========[object Object][object Object]API for working with the ADAF type.[object Object][object Object]Import this module like this:[object Object][object Object][object Object][object Object][object Object]The ADAF structure[object Object]------------------[object Object][object Object]An ADAF consists of three parts: meta data, results, and timeseries.[object Object][object Object]Meta data contains information about the data in the ADAF. Stuff like when,[object Object]where and how it was measured or what parameter values were used to generated[object Object]it. A general guideline is that the meta data should be enough to (at least in[object Object]theory) reproduce the data in the ADAF.[object Object][object Object]Results and timeseries contain the actual data. Results are always scalar[object Object]whereas the timeseries can have any number of values.[object Object][object Object]Timeseries can come in several systems and each system can contain several[object Object]rasters. Each raster in turn has one basis and any number of timeseries. So[object Object]for example an experiment where some signals are sampled at 100Hz and others[object Object]are sampled only once per second would have (at least) two rasters. A basis[object Object]doesn’t have to be uniform but can have samples on...	`1`
`What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?`	# Node[object Object][object Object]Table to ADAF[object Object]=============[object Object][object Object]Convert a Table into an ADAF, placing its content in the specified container.[object Object][object Object]Documentation[object Object]-------------[object Object][object Object]The target container in the ADAF is specified in the configuration GUI. If the[object Object]timeseries container is chosen it is necessary to specify the column in the[object Object]Table which will be the time basis signal in the ADAF. You can also specify[object Object]the name of the system and raster containers.[object Object][object Object]See also Working with ADAF for tips about how to use these conversion[object Object]nodes.[object Object][object Object]	`0`
`What are the three primary parts of an ADAF, and how does the meta data differ functionally from the results and timeseries in terms of reproducibility?`	# Node[object Object][object Object]Select columns in ADAF with structure Table[object Object]===========================================[object Object][object Object]Select the columns to keep in ADAF using selection table created by ADAF structure to table[object Object][object Object]Documentation[object Object]-------------[object Object][object Object]Use this node if you’re only interested in some of the data in an ADAF[object Object]e.g. for performance reasons.[object Object][object Object]The Table/Tables argument should have four columns, which must be named[object Object]Type, System, Raster, and Parameter. These columns hold the names of the[object Object]corresponding fields in the ADAF/ADAFs.[object Object][object Object]Definition[object Object]----------[object Object][object Object]### Input ports[object Object][object Object]> [object Object][object Object]> : Type: table[object Object]> ADAF structure selection[object Object]> [object Object]> [object Object][object Object]> : Type: adaf[object Object]> ADAF data matched with selection[object Object][object Object]### Output ports[object Object][object Object]> [object Object][object Object]> : Type: adaf[object Object]> ADAF data after selection[object Object][object Object]### Configuration[object Object][object Object]> [object Object] (complement)[object Object]> : When enabled, the selected columns will be removed. When disabled, the non-selected columns will be removed.[object Object][object Object]### Related nodes[object Object][object Object]* Select columns in ADAFs with structure Table[object Object]* Sel...	`0`

Loss: BinaryCrossEntropyLoss with these parameters:
json
1{ 2 "activation_fn": "torch.nn.modules.linear.Identity", 3 "pos_weight": 3 4}

anc-pos-neg

Dataset: anc-pos-neg
Size: 2,435 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 14 characters mean: 158.34 characters max: 317 characters	min: 163 characters mean: 2474.63 characters max: 20435 characters	min: 214 characters mean: 1745.03 characters max: 20435 characters

Samples:

anchor	positive	negative
`- Retrieve the time ([object Object]) and signal ([object Object]) values of [object Object]?`	# API ADAF API[object Object][object Object]Accessing the data[object Object]------------------[object Object][object Object]The [object Object] object has two members called [object Object] and [object Object]containing the meta data and results respectively. Both are sympathy.api.adaf.Groupobjects.[object Object][object Object]Example of how to use [object Object] ([object Object] is completely analogous):[object Object]: [object Object][object Object][object Object]Timeseries can be accessed in two different ways. Either via the member[object Object] or via the member [object Object]. Using sys is generally recommended since[object Object] handles multiple timeseries with the same name across different rasters[object Object]poorly.[object Object][object Object]Example of how to use sys:[object Object]: ```[object Object]>>> f.sys.create('Measurement system')[object Object]>>> f.sys['Measurement system'].create('Raster1')[object Object]>>> f.sys['Measurement system']['Raster...	# API ADAF API[object Object][object Object]Class sympathy.api.adaf.Timeseries[object Object]----------------------------------[object Object][object Object][object Object] sympathy.api.adaf.Timeseries([object Object], [object Object], [object Object])[object Object]: Class representing a timeseries. The values in the timeseries can be[object Object]accessed as a numpy array via the member [object Object]. The timeseries is also[object Object]connected to a time basis whose values can be accessed as a numpy array[object Object]via the property [object Object].[object Object][object Object]The timeseries can also have any number of attributes. The methodssympathy.api.adaf.Timeseries.unit and sympathy.api.adaf.Timeseries.description retrieve those two attributes. To get[object Object]all attributes use the method sympathy.api.adaf.Timeseries.get_attributes.[object Object][object Object]basis() → sympathy.typeutils.adaf.Column[object Object]: Return the timeseries data basis as a sympathy.api.adaf.Column.[object Object][object Object]description() → str[object Object]: Return the description attribute or an empty string if it is not set.[object Object][object Object][object Object] dtype: dtype[object Object]: dtype of timeseries.[object Object][object Object]get_attributes() → Dict[str, int \
`How can you add a custom attribute (e.g., [object Object]) to a signal named [object Object] in a raster, and how would you later retrieve this attribute?`	# API ADAF API[object Object][object Object]Accessing the data[object Object]------------------[object Object][object Object]The [object Object] object has two members called [object Object] and [object Object]containing the meta data and results respectively. Both are sympathy.api.adaf.Groupobjects.[object Object][object Object]Example of how to use [object Object] ([object Object] is completely analogous):[object Object]: [object Object][object Object][object Object]Timeseries can be accessed in two different ways. Either via the member[object Object] or via the member [object Object]. Using sys is generally recommended since[object Object] handles multiple timeseries with the same name across different rasters[object Object]poorly.[object Object][object Object]Example of how to use sys:[object Object]: ```[object Object]>>> f.sys.create('Measurement system')[object Object]>>> f.sys['Measurement system'].create('Raster1')[object Object]>>> f.sys['Measurement system']['Raster...	# API ADAF API[object Object][object Object]Class sympathy.api.adaf.RasterN[object Object]-------------------------------[object Object][object Object][object Object] sympathy.api.adaf.RasterN([object Object], [object Object], [object Object])[object Object]: Represents a raster with a single time basis and any number of timeseries[object Object]columns.[object Object][object Object][object Object] attr: Attributes[object Object]: Raster level attributes.[object Object][object Object]basis_column() → sympathy.typeutils.adaf.Column[object Object]: Return the time basis for this raster. The returned object is of typesympathy.api.adaf.Column.[object Object][object Object]create_basis([object Object], *attributes: Dict[str, int \
`How can you add a custom attribute (e.g., [object Object]) to a signal named [object Object] in a raster, and how would you later retrieve this attribute?`	# API ADAF API[object Object][object Object]Accessing the data[object Object]------------------[object Object][object Object]The [object Object] object has two members called [object Object] and [object Object]containing the meta data and results respectively. Both are sympathy.api.adaf.Groupobjects.[object Object][object Object]Example of how to use [object Object] ([object Object] is completely analogous):[object Object]: [object Object][object Object][object Object]Timeseries can be accessed in two different ways. Either via the member[object Object] or via the member [object Object]. Using sys is generally recommended since[object Object] handles multiple timeseries with the same name across different rasters[object Object]poorly.[object Object][object Object]Example of how to use sys:[object Object]: ```[object Object]>>> f.sys.create('Measurement system')[object Object]>>> f.sys['Measurement system'].create('Raster1')[object Object]>>> f.sys['Measurement system']['Raster...	# API ADAF API[object Object][object Object]Class sympathy.api.adaf.Timeseries[object Object]----------------------------------[object Object][object Object][object Object] sympathy.api.adaf.Timeseries([object Object], [object Object], [object Object])[object Object]: Class representing a timeseries. The values in the timeseries can be[object Object]accessed as a numpy array via the member [object Object]. The timeseries is also[object Object]connected to a time basis whose values can be accessed as a numpy array[object Object]via the property [object Object].[object Object][object Object]The timeseries can also have any number of attributes. The methodssympathy.api.adaf.Timeseries.unit and sympathy.api.adaf.Timeseries.description retrieve those two attributes. To get[object Object]all attributes use the method sympathy.api.adaf.Timeseries.get_attributes.[object Object][object Object]basis() → sympathy.typeutils.adaf.Column[object Object]: Return the timeseries data basis as a sympathy.api.adaf.Column.[object Object][object Object]description() → str[object Object]: Return the description attribute or an empty string if it is not set.[object Object][object Object][object Object] dtype: dtype[object Object]: dtype of timeseries.[object Object][object Object]get_attributes() → Dict[str, int \

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 10.0, 3 "num_negatives": 4, 4 "activation_fn": "torch.nn.modules.activation.Sigmoid", 5 "mini_batch_size": 32 6}

anc-pos-neg-2

Dataset: anc-pos-neg-2
Size: 1,219 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 0 characters mean: 89.95 characters max: 143 characters	min: 496 characters mean: 1890.31 characters max: 7315 characters	min: 160 characters mean: 2026.14 characters max: 12851 characters

Samples:

anchor	positive	negative
`Is it possible to run a Node.js script or environment from within a Python program?`	# Nodes in python[object Object][object Object]Working with nodes[object Object]------------------[object Object][object Object]Nodes store the changes made during configure and when the parameters are[object Object]changed. They produce a list of data elements when executed and expect a list of[object Object]data elements as input, this makes it possible to easily connect the data[object Object]between nodes. Note that the ordering of inputs and outputs is important and[object Object]should match the declaration order in the node definition.[object Object][object Object]The code example below demonstrates how to use the result produced by one node as[object Object]input for another.[object Object][object Object][object Object][object Object]The code example below demonstrates how to use the result produced by multiple[object Object]nodes as input for another.[object Object][object Object]```[object Object]random_table0 = library.node('Random Table')[object Object]rt_output0 = random_table.execute()[object Object][object Object]random_table1 = library.node('Random Table')[object Object]rt_outpu...	[object Object][object Object]Nodes[object Object]=====[object Object][object Object]A node is defined as a Python class which inherits from[object Object]. All node definitions should be in files with[object Object]filenames matching [object Object] and be placed in the nodes folder of a node[object Object]library. See Libraries for information about where to put nodes in your[object Object]library. Nodes can be placed in subfolders and multiple nodes can be defined in[object Object]the same file.[object Object][object Object]Node definition[object Object]---------------[object Object][object Object]The following class variables make up the definition of a node.[object Object][object Object]Note[object Object][object Object]The fields [object Object] and [object Object] are needed to generate the node. If any[object Object]of these two are missing any attempt at creating this node stops immediately[object Object]without any error message. This can be a good way of e.g. creating a[object Object]superclass for multiple node classes.[object Object][object Object][object Object][object Object]: [object Object].[object Object][object Object]The name of the node, is what the user will rely on to identify the node. It[object Object]will show in the library view and in the node’s tooltip. It will also be used[object Object]as the default label of any instance of the node in a flow.[object Object][object Object]Try to keep the ...
`Is it possible to run a Node.js script or environment from within a Python program?`	# Nodes in python[object Object][object Object]Working with nodes[object Object]------------------[object Object][object Object]Nodes store the changes made during configure and when the parameters are[object Object]changed. They produce a list of data elements when executed and expect a list of[object Object]data elements as input, this makes it possible to easily connect the data[object Object]between nodes. Note that the ordering of inputs and outputs is important and[object Object]should match the declaration order in the node definition.[object Object][object Object]The code example below demonstrates how to use the result produced by one node as[object Object]input for another.[object Object][object Object][object Object][object Object]The code example below demonstrates how to use the result produced by multiple[object Object]nodes as input for another.[object Object][object Object]```[object Object]random_table0 = library.node('Random Table')[object Object]rt_output0 = random_table.execute()[object Object][object Object]random_table1 = library.node('Random Table')[object Object]rt_outpu...	# Nodes in python[object Object][object Object]Reference[object Object]---------[object Object][object Object][object Object] sympathy.app.interactive.InteractiveNotNodeError[source][object Object][object Object][object Object] sympathy.app.interactive.SyiLibrary([object Object], [object Object], [object Object], [object Object])[source][object Object]: A library of nodes that can be configured and executed in Python code.[object Object][object Object]Should not be instantiated directly. Instead call sympathy.app.interactive.load_library.[object Object][object Object]node([object Object], [object Object]) → sympathy.app.interactive.SyiNode[source][object Object]: Attempt to find [object Object] in the library.[object Object][object Object]Argument [object Object] can be either a node id or a node name. If no matching[object Object]node can be found a KeyError is raised.[object Object][object Object]If [object Object] is [object Object] (the default) and [object Object] doesn’t match any[object Object]node exactly, it is used as a pattern that the node name must match.[object Object]The characters of the pattern must appear in the node name in the same[object Object]order as in the pattern, but must not be of the same case, and may have[object Object]other characters in between them. If multiple nodes match the pattern a[object Object]KeyError is raised.[object Object][object Object]nodeids() ...
`Is it possible to run a Node.js script or environment from within a Python program?`	# Nodes in python[object Object][object Object]Working with nodes[object Object]------------------[object Object][object Object]Nodes store the changes made during configure and when the parameters are[object Object]changed. They produce a list of data elements when executed and expect a list of[object Object]data elements as input, this makes it possible to easily connect the data[object Object]between nodes. Note that the ordering of inputs and outputs is important and[object Object]should match the declaration order in the node definition.[object Object][object Object]The code example below demonstrates how to use the result produced by one node as[object Object]input for another.[object Object][object Object][object Object][object Object]The code example below demonstrates how to use the result produced by multiple[object Object]nodes as input for another.[object Object][object Object]```[object Object]random_table0 = library.node('Random Table')[object Object]rt_output0 = random_table.execute()[object Object][object Object]random_table1 = library.node('Random Table')[object Object]rt_outpu...	`# API[object Object][object Object]Datasource API[object Object]==============[object Object][object Object]API for working with the Datasource type.[object Object][object Object]Import this module like this:[object Object][object Object][object Object][object Object]Class [object Object][object Object]-----------------------------[object Object][object Object][object Object] sympathy.api.datasource.Datasource(*filename: str \`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 10.0, 3 "num_negatives": 4, 4 "activation_fn": "torch.nn.modules.activation.Sigmoid", 5 "mini_batch_size": 32 6}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 0.0001
num_train_epochs: 10
warmup_ratio: 0.1
dataloader_num_workers: 4
load_best_model_at_end: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 4
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Click to expand

Epoch	Step	Training Loss	sydoc-tester_ndcg@10	NanoMSMARCO_R100_ndcg@10	NanoNFCorpus_R100_ndcg@10	NanoNQ_R100_ndcg@10	NanoBEIR_R100_mean_ndcg@10
-1	-1	-	0.2780 (+0.1285)	0.6686 (+0.1282)	0.3930 (+0.0680)	0.7599 (+0.2592)	0.6072 (+0.1518)
0.0167	1	1.1888	-	-	-	-	-
0.0333	2	1.8501	-	-	-	-	-
0.05	3	3.0206	-	-	-	-	-
0.0667	4	1.5729	-	-	-	-	-
0.0833	5	1.8201	-	-	-	-	-
0.1	6	2.7519	-	-	-	-	-
0.1167	7	1.7264	-	-	-	-	-
0.1333	8	1.9018	-	-	-	-	-
0.15	9	2.5682	-	-	-	-	-
0.1667	10	2.6998	-	-	-	-	-
0.1833	11	2.0299	-	-	-	-	-
0.2	12	2.7956	-	-	-	-	-
0.2167	13	0.6817	-	-	-	-	-
0.2333	14	1.838	-	-	-	-	-
0.25	15	2.2811	-	-	-	-	-
0.2667	16	1.3663	-	-	-	-	-
0.2833	17	2.0837	-	-	-	-	-
0.3	18	2.4574	-	-	-	-	-
0.3167	19	0.23	-	-	-	-	-
0.3333	20	1.8395	-	-	-	-	-
0.35	21	2.4167	-	-	-	-	-
0.3667	22	0.6286	-	-	-	-	-
0.3833	23	1.8573	-	-	-	-	-
0.4	24	2.3595	-	-	-	-	-
0.4167	25	0.5143	-	-	-	-	-
0.4333	26	1.4291	-	-	-	-	-
0.45	27	2.0018	-	-	-	-	-
0.4667	28	0.1993	-	-	-	-	-
0.4833	29	1.7079	-	-	-	-	-
0.5	30	1.9053	-	-	-	-	-
0.5167	31	0.6029	-	-	-	-	-
0.5333	32	1.4611	-	-	-	-	-
0.55	33	2.0044	-	-	-	-	-
0.5667	34	0.4241	-	-	-	-	-
0.5833	35	2.071	-	-	-	-	-
0.6	36	2.0503	-	-	-	-	-
0.6167	37	1.0458	-	-	-	-	-
0.6333	38	1.5994	-	-	-	-	-
0.65	39	1.868	-	-	-	-	-
0.6667	40	0.5284	-	-	-	-	-
0.6833	41	1.3488	-	-	-	-	-
0.7	42	1.9041	-	-	-	-	-
0.7167	43	0.5827	-	-	-	-	-
0.7333	44	1.3666	-	-	-	-	-
0.75	45	2.1058	-	-	-	-	-
0.7667	46	0.6255	-	-	-	-	-
0.7833	47	1.0372	-	-	-	-	-
0.8	48	2.2852	-	-	-	-	-
0.8167	49	0.5618	-	-	-	-	-
0.8333	50	1.1474	-	-	-	-	-
0.85	51	2.1265	-	-	-	-	-
0.8667	52	0.4827	-	-	-	-	-
0.8833	53	1.2651	-	-	-	-	-
0.9	54	1.8336	-	-	-	-	-
0.9167	55	0.7961	-	-	-	-	-
0.9333	56	1.0884	-	-	-	-	-
0.95	57	1.6975	-	-	-	-	-
0.9667	58	0.5475	-	-	-	-	-
0.9833	59	0.8953	-	-	-	-	-
1.0	60	1.8382	0.2914 (+0.1420)	0.6658 (+0.1254)	0.4003 (+0.0752)	0.7547 (+0.2540)	0.6069 (+0.1516)
1.0167	61	0.5987	-	-	-	-	-
1.0333	62	1.0246	-	-	-	-	-
1.05	63	1.6712	-	-	-	-	-
1.0667	64	0.4722	-	-	-	-	-
1.0833	65	1.1193	-	-	-	-	-
1.1	66	1.5013	-	-	-	-	-
1.1167	67	0.5394	-	-	-	-	-
1.1333	68	1.1887	-	-	-	-	-
1.15	69	1.7034	-	-	-	-	-
1.1667	70	0.4565	-	-	-	-	-
1.1833	71	1.2703	-	-	-	-	-
1.2	72	1.753	-	-	-	-	-
1.2167	73	0.3727	-	-	-	-	-
1.2333	74	0.8781	-	-	-	-	-
1.25	75	1.6562	-	-	-	-	-
1.2667	76	0.7796	-	-	-	-	-
1.2833	77	1.0529	-	-	-	-	-
1.3	78	1.5911	-	-	-	-	-
1.3167	79	0.3978	-	-	-	-	-
1.3333	80	0.8815	-	-	-	-	-
1.35	81	1.6555	-	-	-	-	-
1.3667	82	0.4231	-	-	-	-	-
1.3833	83	0.8421	-	-	-	-	-
1.4	84	1.78	-	-	-	-	-
1.4167	85	0.4604	-	-	-	-	-
1.4333	86	1.4535	-	-	-	-	-
1.45	87	1.5948	-	-	-	-	-
1.4667	88	1.0813	-	-	-	-	-
1.4833	89	0.9153	-	-	-	-	-
1.5	90	1.3446	-	-	-	-	-
1.5167	91	0.8085	-	-	-	-	-
1.5333	92	0.8611	-	-	-	-	-
1.55	93	2.0656	-	-	-	-	-
1.5667	94	0.8703	-	-	-	-	-
1.5833	95	1.0746	-	-	-	-	-
1.6	96	1.8937	-	-	-	-	-
1.6167	97	0.3555	-	-	-	-	-
1.6333	98	0.9181	-	-	-	-	-
1.65	99	1.666	-	-	-	-	-
1.6667	100	0.5811	-	-	-	-	-
1.6833	101	0.8751	-	-	-	-	-
1.7	102	1.4337	-	-	-	-	-
1.7167	103	0.5711	-	-	-	-	-
1.7333	104	0.8895	-	-	-	-	-
1.75	105	1.5261	-	-	-	-	-
1.7667	106	0.4124	-	-	-	-	-
1.7833	107	1.0844	-	-	-	-	-
1.8	108	1.3582	-	-	-	-	-
1.8167	109	0.6696	-	-	-	-	-
1.8333	110	1.014	-	-	-	-	-
1.85	111	1.8169	-	-	-	-	-
1.8667	112	0.4394	-	-	-	-	-
1.8833	113	0.8345	-	-	-	-	-
1.9	114	1.3999	-	-	-	-	-
1.9167	115	0.1797	-	-	-	-	-
1.9333	116	0.8217	-	-	-	-	-
1.95	117	1.2372	-	-	-	-	-
1.9667	118	0.3477	-	-	-	-	-
1.9833	119	0.9426	-	-	-	-	-
2.0	120	0.7439	0.3266 (+0.1771)	0.6720 (+0.1315)	0.4090 (+0.0840)	0.7295 (+0.2289)	0.6035 (+0.1482)
2.0167	121	0.5735	-	-	-	-	-
2.0333	122	1.0874	-	-	-	-	-
2.05	123	1.5375	-	-	-	-	-
2.0667	124	0.4699	-	-	-	-	-
2.0833	125	0.6828	-	-	-	-	-
2.1	126	1.1029	-	-	-	-	-
2.1167	127	0.2952	-	-	-	-	-
2.1333	128	0.7866	-	-	-	-	-
2.15	129	1.1173	-	-	-	-	-
2.1667	130	0.4053	-	-	-	-	-
2.1833	131	0.8136	-	-	-	-	-
2.2	132	1.1145	-	-	-	-	-
2.2167	133	0.2084	-	-	-	-	-
2.2333	134	0.6429	-	-	-	-	-
2.25	135	1.0727	-	-	-	-	-
2.2667	136	0.2806	-	-	-	-	-
2.2833	137	0.7038	-	-	-	-	-
2.3	138	1.3219	-	-	-	-	-
2.3167	139	0.3426	-	-	-	-	-
2.3333	140	0.939	-	-	-	-	-
2.35	141	1.3082	-	-	-	-	-
2.3667	142	0.4325	-	-	-	-	-
2.3833	143	0.8041	-	-	-	-	-
2.4	144	1.2372	-	-	-	-	-
2.4167	145	0.3477	-	-	-	-	-
2.4333	146	0.6534	-	-	-	-	-
2.45	147	0.9268	-	-	-	-	-
2.4667	148	0.1559	-	-	-	-	-
2.4833	149	0.8769	-	-	-	-	-
2.5	150	0.8099	-	-	-	-	-
2.5167	151	0.1916	-	-	-	-	-
2.5333	152	0.9749	-	-	-	-	-
2.55	153	0.8685	-	-	-	-	-
2.5667	154	0.4233	-	-	-	-	-
2.5833	155	0.7877	-	-	-	-	-
2.6	156	1.0647	-	-	-	-	-
2.6167	157	0.3441	-	-	-	-	-
2.6333	158	0.8019	-	-	-	-	-
2.65	159	0.8691	-	-	-	-	-
2.6667	160	0.2585	-	-	-	-	-
2.6833	161	0.7472	-	-	-	-	-
2.7	162	0.8618	-	-	-	-	-
2.7167	163	0.2301	-	-	-	-	-
2.7333	164	0.6078	-	-	-	-	-
2.75	165	0.8942	-	-	-	-	-
2.7667	166	0.3613	-	-	-	-	-
2.7833	167	0.6139	-	-	-	-	-
2.8	168	0.8171	-	-	-	-	-
2.8167	169	0.2423	-	-	-	-	-
2.8333	170	0.7126	-	-	-	-	-
2.85	171	0.8464	-	-	-	-	-
2.8667	172	0.2323	-	-	-	-	-
2.8833	173	0.5863	-	-	-	-	-
2.9	174	0.9001	-	-	-	-	-
2.9167	175	0.3677	-	-	-	-	-
2.9333	176	0.6953	-	-	-	-	-
2.95	177	0.816	-	-	-	-	-
2.9667	178	0.1606	-	-	-	-	-
2.9833	179	0.4495	-	-	-	-	-
3.0	180	0.5979	0.3271 (+0.1777)	0.6738 (+0.1333)	0.4114 (+0.0864)	0.7131 (+0.2125)	0.5994 (+0.1441)
3.0167	181	0.2455	-	-	-	-	-
3.0333	182	0.8384	-	-	-	-	-
3.05	183	0.7267	-	-	-	-	-
3.0667	184	0.8089	-	-	-	-	-
3.0833	185	0.5904	-	-	-	-	-
3.1	186	0.6173	-	-	-	-	-
3.1167	187	0.3746	-	-	-	-	-
3.1333	188	0.4729	-	-	-	-	-
3.15	189	0.7779	-	-	-	-	-
3.1667	190	0.323	-	-	-	-	-
3.1833	191	0.5322	-	-	-	-	-
3.2	192	0.6053	-	-	-	-	-
3.2167	193	0.4589	-	-	-	-	-
3.2333	194	0.5053	-	-	-	-	-
3.25	195	0.7136	-	-	-	-	-
3.2667	196	0.296	-	-	-	-	-
3.2833	197	0.631	-	-	-	-	-
3.3	198	0.8061	-	-	-	-	-
3.3167	199	0.2414	-	-	-	-	-
3.3333	200	0.6171	-	-	-	-	-
3.35	201	0.5376	-	-	-	-	-
3.3667	202	0.5552	-	-	-	-	-
3.3833	203	0.6648	-	-	-	-	-
3.4	204	0.7012	-	-	-	-	-
3.4167	205	0.4025	-	-	-	-	-
3.4333	206	0.5783	-	-	-	-	-
3.45	207	0.4234	-	-	-	-	-
3.4667	208	0.5073	-	-	-	-	-
3.4833	209	0.6345	-	-	-	-	-
3.5	210	0.6181	-	-	-	-	-
3.5167	211	0.2886	-	-	-	-	-
3.5333	212	0.4679	-	-	-	-	-
3.55	213	0.3889	-	-	-	-	-
3.5667	214	0.2376	-	-	-	-	-
3.5833	215	0.7177	-	-	-	-	-
3.6	216	0.4891	-	-	-	-	-
3.6167	217	0.3411	-	-	-	-	-
3.6333	218	0.8069	-	-	-	-	-
3.65	219	0.8119	-	-	-	-	-
3.6667	220	0.4792	-	-	-	-	-
3.6833	221	0.8323	-	-	-	-	-
3.7	222	0.7516	-	-	-	-	-
3.7167	223	0.2906	-	-	-	-	-
3.7333	224	0.5762	-	-	-	-	-
3.75	225	0.6405	-	-	-	-	-
3.7667	226	0.1347	-	-	-	-	-
3.7833	227	0.4869	-	-	-	-	-
3.8	228	0.5139	-	-	-	-	-
3.8167	229	0.2649	-	-	-	-	-
3.8333	230	0.7511	-	-	-	-	-
3.85	231	0.552	-	-	-	-	-
3.8667	232	0.2641	-	-	-	-	-
3.8833	233	0.3692	-	-	-	-	-
3.9	234	0.6599	-	-	-	-	-
3.9167	235	0.9202	-	-	-	-	-
3.9333	236	0.6013	-	-	-	-	-
3.95	237	0.6525	-	-	-	-	-
3.9667	238	0.3979	-	-	-	-	-
3.9833	239	0.5321	-	-	-	-	-
4.0	240	0.0005	0.3370 (+0.1876)	0.6507 (+0.1103)	0.4011 (+0.0760)	0.6923 (+0.1917)	0.5814 (+0.1260)
4.0167	241	0.1341	-	-	-	-	-
4.0333	242	0.5269	-	-	-	-	-
4.05	243	0.6917	-	-	-	-	-
4.0667	244	0.437	-	-	-	-	-
4.0833	245	0.5446	-	-	-	-	-
4.1	246	0.5892	-	-	-	-	-
4.1167	247	0.2742	-	-	-	-	-
4.1333	248	0.5049	-	-	-	-	-
4.15	249	0.7015	-	-	-	-	-
4.1667	250	0.2648	-	-	-	-	-
4.1833	251	0.5977	-	-	-	-	-
4.2	252	0.8432	-	-	-	-	-
4.2167	253	0.281	-	-	-	-	-
4.2333	254	0.5203	-	-	-	-	-
4.25	255	0.6649	-	-	-	-	-
4.2667	256	0.1843	-	-	-	-	-
4.2833	257	0.4616	-	-	-	-	-
4.3	258	0.3689	-	-	-	-	-
4.3167	259	0.2484	-	-	-	-	-
4.3333	260	0.4718	-	-	-	-	-
4.35	261	0.5886	-	-	-	-	-
4.3667	262	0.1984	-	-	-	-	-
4.3833	263	0.6351	-	-	-	-	-
4.4	264	0.4616	-	-	-	-	-
4.4167	265	0.3106	-	-	-	-	-
4.4333	266	0.5568	-	-	-	-	-
4.45	267	0.3814	-	-	-	-	-
4.4667	268	0.2351	-	-	-	-	-
4.4833	269	0.548	-	-	-	-	-
4.5	270	0.5559	-	-	-	-	-
4.5167	271	0.2272	-	-	-	-	-
4.5333	272	0.5367	-	-	-	-	-
4.55	273	0.4771	-	-	-	-	-
4.5667	274	0.5025	-	-	-	-	-
4.5833	275	0.4496	-	-	-	-	-
4.6	276	0.3119	-	-	-	-	-
4.6167	277	0.1054	-	-	-	-	-
4.6333	278	0.5954	-	-	-	-	-
4.65	279	0.5023	-	-	-	-	-
4.6667	280	0.1567	-	-	-	-	-
4.6833	281	0.5903	-	-	-	-	-
4.7	282	0.5529	-	-	-	-	-
4.7167	283	0.5897	-	-	-	-	-
4.7333	284	0.4256	-	-	-	-	-
4.75	285	0.3928	-	-	-	-	-
4.7667	286	0.2755	-	-	-	-	-
4.7833	287	0.5036	-	-	-	-	-
4.8	288	0.464	-	-	-	-	-
4.8167	289	0.1169	-	-	-	-	-
4.8333	290	0.6028	-	-	-	-	-
4.85	291	0.2327	-	-	-	-	-
4.8667	292	0.6823	-	-	-	-	-
4.8833	293	0.5122	-	-	-	-	-
4.9	294	0.4079	-	-	-	-	-
4.9167	295	0.4138	-	-	-	-	-
4.9333	296	0.6886	-	-	-	-	-
4.95	297	0.2706	-	-	-	-	-
4.9667	298	0.2255	-	-	-	-	-
4.9833	299	0.4051	-	-	-	-	-
5.0	300	0.4815	0.3403 (+0.1909)	0.6408 (+0.1003)	0.4042 (+0.0791)	0.7126 (+0.2119)	0.5858 (+0.1305)
5.0167	301	0.1022	-	-	-	-	-
5.0333	302	0.3965	-	-	-	-	-
5.05	303	0.3549	-	-	-	-	-
5.0667	304	0.4604	-	-	-	-	-
5.0833	305	0.4974	-	-	-	-	-
5.1	306	0.5253	-	-	-	-	-
5.1167	307	0.1403	-	-	-	-	-
5.1333	308	0.554	-	-	-	-	-
5.15	309	0.4808	-	-	-	-	-
5.1667	310	0.3776	-	-	-	-	-
5.1833	311	0.5058	-	-	-	-	-
5.2	312	0.5046	-	-	-	-	-
5.2167	313	0.0419	-	-	-	-	-
5.2333	314	0.5171	-	-	-	-	-
5.25	315	0.2989	-	-	-	-	-
5.2667	316	0.1901	-	-	-	-	-
5.2833	317	0.4728	-	-	-	-	-
5.3	318	0.5452	-	-	-	-	-
5.3167	319	0.3045	-	-	-	-	-
5.3333	320	0.4575	-	-	-	-	-
5.35	321	0.4383	-	-	-	-	-
5.3667	322	0.367	-	-	-	-	-
5.3833	323	0.6289	-	-	-	-	-
5.4	324	0.5697	-	-	-	-	-
5.4167	325	0.3275	-	-	-	-	-
5.4333	326	0.6355	-	-	-	-	-
5.45	327	0.2026	-	-	-	-	-
5.4667	328	0.3994	-	-	-	-	-
5.4833	329	0.6455	-	-	-	-	-
5.5	330	0.293	-	-	-	-	-
5.5167	331	0.6003	-	-	-	-	-
5.5333	332	0.46	-	-	-	-	-
5.55	333	0.291	-	-	-	-	-
5.5667	334	0.2577	-	-	-	-	-
5.5833	335	0.4286	-	-	-	-	-
5.6	336	0.5138	-	-	-	-	-
5.6167	337	0.4342	-	-	-	-	-
5.6333	338	0.7158	-	-	-	-	-
5.65	339	0.3723	-	-	-	-	-
5.6667	340	0.3464	-	-	-	-	-
5.6833	341	0.5797	-	-	-	-	-
5.7	342	0.3321	-	-	-	-	-
5.7167	343	0.4743	-	-	-	-	-
5.7333	344	0.4901	-	-	-	-	-
5.75	345	0.4753	-	-	-	-	-
5.7667	346	0.4173	-	-	-	-	-
5.7833	347	0.291	-	-	-	-	-
5.8	348	0.2717	-	-	-	-	-
5.8167	349	0.237	-	-	-	-	-
5.8333	350	0.5443	-	-	-	-	-
5.85	351	0.3157	-	-	-	-	-
5.8667	352	0.1993	-	-	-	-	-
5.8833	353	0.4968	-	-	-	-	-
5.9	354	0.4172	-	-	-	-	-
5.9167	355	0.1981	-	-	-	-	-
5.9333	356	0.4192	-	-	-	-	-
5.95	357	0.3236	-	-	-	-	-
5.9667	358	0.3602	-	-	-	-	-
5.9833	359	0.4311	-	-	-	-	-
6.0	360	0.4171	0.3336 (+0.1842)	0.6444 (+0.1040)	0.4074 (+0.0824)	0.7000 (+0.1994)	0.5840 (+0.1286)
6.0167	361	0.2868	-	-	-	-	-
6.0333	362	0.5633	-	-	-	-	-
6.05	363	0.4367	-	-	-	-	-
6.0667	364	0.4977	-	-	-	-	-
6.0833	365	0.6418	-	-	-	-	-
6.1	366	0.2547	-	-	-	-	-
6.1167	367	0.3511	-	-	-	-	-
6.1333	368	0.5132	-	-	-	-	-
6.15	369	0.3701	-	-	-	-	-
6.1667	370	0.2419	-	-	-	-	-
6.1833	371	0.3204	-	-	-	-	-
6.2	372	0.3631	-	-	-	-	-
6.2167	373	0.3157	-	-	-	-	-
6.2333	374	0.5016	-	-	-	-	-
6.25	375	0.297	-	-	-	-	-
6.2667	376	0.4432	-	-	-	-	-
6.2833	377	0.345	-	-	-	-	-
6.3	378	0.3711	-	-	-	-	-
6.3167	379	0.5635	-	-	-	-	-
6.3333	380	0.3848	-	-	-	-	-
6.35	381	0.1937	-	-	-	-	-
6.3667	382	0.1609	-	-	-	-	-
6.3833	383	0.4873	-	-	-	-	-
6.4	384	0.3656	-	-	-	-	-
6.4167	385	0.0947	-	-	-	-	-
6.4333	386	0.3603	-	-	-	-	-
6.45	387	0.4195	-	-	-	-	-
6.4667	388	0.2649	-	-	-	-	-
6.4833	389	0.3971	-	-	-	-	-
6.5	390	0.2258	-	-	-	-	-
6.5167	391	0.1702	-	-	-	-	-
6.5333	392	0.3994	-	-	-	-	-
6.55	393	0.3631	-	-	-	-	-
6.5667	394	0.1625	-	-	-	-	-
6.5833	395	0.375	-	-	-	-	-
6.6	396	0.3067	-	-	-	-	-
6.6167	397	0.116	-	-	-	-	-
6.6333	398	0.3915	-	-	-	-	-
6.65	399	0.2512	-	-	-	-	-
6.6667	400	0.5099	-	-	-	-	-
6.6833	401	0.3622	-	-	-	-	-
6.7	402	0.2473	-	-	-	-	-
6.7167	403	0.3713	-	-	-	-	-
6.7333	404	0.4604	-	-	-	-	-
6.75	405	0.4876	-	-	-	-	-
6.7667	406	0.0745	-	-	-	-	-
6.7833	407	0.4345	-	-	-	-	-
6.8	408	0.3579	-	-	-	-	-
6.8167	409	0.2141	-	-	-	-	-
6.8333	410	0.5035	-	-	-	-	-
6.85	411	0.2538	-	-	-	-	-
6.8667	412	0.329	-	-	-	-	-
6.8833	413	0.338	-	-	-	-	-
6.9	414	0.4243	-	-	-	-	-
6.9167	415	0.3974	-	-	-	-	-
6.9333	416	0.486	-	-	-	-	-
6.95	417	0.1896	-	-	-	-	-
6.9667	418	0.2265	-	-	-	-	-
6.9833	419	0.4796	-	-	-	-	-
7.0	420	0.7441	0.3388 (+0.1894)	0.6231 (+0.0827)	0.3935 (+0.0684)	0.6922 (+0.1916)	0.5696 (+0.1142)
7.0167	421	0.0353	-	-	-	-	-
7.0333	422	0.5483	-	-	-	-	-
7.05	423	0.4845	-	-	-	-	-
7.0667	424	0.4536	-	-	-	-	-
7.0833	425	0.3831	-	-	-	-	-
7.1	426	0.297	-	-	-	-	-
7.1167	427	0.1597	-	-	-	-	-
7.1333	428	0.5623	-	-	-	-	-
7.15	429	0.2996	-	-	-	-	-
7.1667	430	0.2648	-	-	-	-	-
7.1833	431	0.4407	-	-	-	-	-
7.2	432	0.2885	-	-	-	-	-
7.2167	433	0.2438	-	-	-	-	-
7.2333	434	0.4212	-	-	-	-	-
7.25	435	0.3673	-	-	-	-	-
7.2667	436	0.3299	-	-	-	-	-
7.2833	437	0.402	-	-	-	-	-
7.3	438	0.2375	-	-	-	-	-
7.3167	439	0.329	-	-	-	-	-
7.3333	440	0.5249	-	-	-	-	-
7.35	441	0.3656	-	-	-	-	-
7.3667	442	0.3228	-	-	-	-	-
7.3833	443	0.4069	-	-	-	-	-
7.4	444	0.37	-	-	-	-	-
7.4167	445	0.2823	-	-	-	-	-
7.4333	446	0.4723	-	-	-	-	-
7.45	447	0.2711	-	-	-	-	-
7.4667	448	0.0393	-	-	-	-	-
7.4833	449	0.5585	-	-	-	-	-
7.5	450	0.2636	-	-	-	-	-
7.5167	451	0.1146	-	-	-	-	-
7.5333	452	0.4453	-	-	-	-	-
7.55	453	0.3957	-	-	-	-	-
7.5667	454	0.5111	-	-	-	-	-
7.5833	455	0.3581	-	-	-	-	-
7.6	456	0.2948	-	-	-	-	-
7.6167	457	0.0755	-	-	-	-	-
7.6333	458	0.3249	-	-	-	-	-
7.65	459	0.4024	-	-	-	-	-
7.6667	460	0.1671	-	-	-	-	-
7.6833	461	0.4869	-	-	-	-	-
7.7	462	0.1798	-	-	-	-	-
7.7167	463	0.3332	-	-	-	-	-
7.7333	464	0.4123	-	-	-	-	-
7.75	465	0.2245	-	-	-	-	-
7.7667	466	0.3406	-	-	-	-	-
7.7833	467	0.3521	-	-	-	-	-
7.8	468	0.2257	-	-	-	-	-
7.8167	469	0.3469	-	-	-	-	-
7.8333	470	0.3765	-	-	-	-	-
7.85	471	0.2123	-	-	-	-	-
7.8667	472	0.4465	-	-	-	-	-
7.8833	473	0.3888	-	-	-	-	-
7.9	474	0.2459	-	-	-	-	-
7.9167	475	0.7323	-	-	-	-	-
7.9333	476	0.3495	-	-	-	-	-
7.95	477	0.2518	-	-	-	-	-
7.9667	478	0.1534	-	-	-	-	-
7.9833	479	0.2959	-	-	-	-	-
8.0	480	0.07	0.3409 (+0.1915)	0.6194 (+0.0790)	0.3933 (+0.0682)	0.6939 (+0.1933)	0.5689 (+0.1135)
8.0167	481	0.5044	-	-	-	-	-
8.0333	482	0.3476	-	-	-	-	-
8.05	483	0.254	-	-	-	-	-
8.0667	484	0.2724	-	-	-	-	-
8.0833	485	0.4188	-	-	-	-	-
8.1	486	0.1158	-	-	-	-	-
8.1167	487	0.1707	-	-	-	-	-
8.1333	488	0.3424	-	-	-	-	-
8.15	489	0.3508	-	-	-	-	-
8.1667	490	0.1103	-	-	-	-	-
8.1833	491	0.4909	-	-	-	-	-
8.2	492	0.1988	-	-	-	-	-
8.2167	493	0.1158	-	-	-	-	-
8.2333	494	0.4486	-	-	-	-	-
8.25	495	0.2352	-	-	-	-	-
8.2667	496	0.0265	-	-	-	-	-
8.2833	497	0.3565	-	-	-	-	-
8.3	498	0.4176	-	-	-	-	-
8.3167	499	0.1988	-	-	-	-	-
8.3333	500	0.5012	-	-	-	-	-
8.35	501	0.2685	-	-	-	-	-
8.3667	502	0.8838	-	-	-	-	-
8.3833	503	0.2845	-	-	-	-	-
8.4	504	0.172	-	-	-	-	-
8.4167	505	0.1257	-	-	-	-	-
8.4333	506	0.4394	-	-	-	-	-
8.45	507	0.3462	-	-	-	-	-
8.4667	508	0.1913	-	-	-	-	-
8.4833	509	0.3712	-	-	-	-	-
8.5	510	0.3224	-	-	-	-	-
8.5167	511	0.4246	-	-	-	-	-
8.5333	512	0.3068	-	-	-	-	-
8.55	513	0.3086	-	-	-	-	-
8.5667	514	0.5934	-	-	-	-	-
8.5833	515	0.3877	-	-	-	-	-
8.6	516	0.2269	-	-	-	-	-
8.6167	517	0.0762	-	-	-	-	-
8.6333	518	0.4297	-	-	-	-	-
8.65	519	0.3039	-	-	-	-	-
8.6667	520	0.112	-	-	-	-	-
8.6833	521	0.5505	-	-	-	-	-
8.7	522	0.2615	-	-	-	-	-
8.7167	523	0.3927	-	-	-	-	-
8.7333	524	0.5144	-	-	-	-	-
8.75	525	0.2332	-	-	-	-	-
8.7667	526	0.1296	-	-	-	-	-
8.7833	527	0.3209	-	-	-	-	-
8.8	528	0.2175	-	-	-	-	-
8.8167	529	0.1195	-	-	-	-	-
8.8333	530	0.5232	-	-	-	-	-
8.85	531	0.2233	-	-	-	-	-
8.8667	532	0.5163	-	-	-	-	-
8.8833	533	0.3405	-	-	-	-	-
8.9	534	0.2303	-	-	-	-	-
8.9167	535	0.3043	-	-	-	-	-
8.9333	536	0.5338	-	-	-	-	-
8.95	537	0.1804	-	-	-	-	-
8.9667	538	0.5183	-	-	-	-	-
8.9833	539	0.2846	-	-	-	-	-
9.0	540	0.0954	0.3488 (+0.1993)	0.6088 (+0.0683)	0.3953 (+0.0703)	0.6934 (+0.1928)	0.5658 (+0.1105)
9.0167	541	0.4875	-	-	-	-	-
9.0333	542	0.3688	-	-	-	-	-
9.05	543	0.3237	-	-	-	-	-
9.0667	544	0.0898	-	-	-	-	-
9.0833	545	0.2571	-	-	-	-	-
9.1	546	0.3119	-	-	-	-	-
9.1167	547	0.2481	-	-	-	-	-
9.1333	548	0.2996	-	-	-	-	-
9.15	549	0.4057	-	-	-	-	-
9.1667	550	0.4908	-	-	-	-	-
9.1833	551	0.585	-	-	-	-	-
9.2	552	0.2549	-	-	-	-	-
9.2167	553	0.0969	-	-	-	-	-
9.2333	554	0.4962	-	-	-	-	-
9.25	555	0.5536	-	-	-	-	-
9.2667	556	0.3017	-	-	-	-	-
9.2833	557	0.3386	-	-	-	-	-
9.3	558	0.1268	-	-	-	-	-
9.3167	559	0.2953	-	-	-	-	-
9.3333	560	0.4083	-	-	-	-	-
9.35	561	0.2145	-	-	-	-	-
9.3667	562	0.3205	-	-	-	-	-
9.3833	563	0.3553	-	-	-	-	-
9.4	564	0.2183	-	-	-	-	-
9.4167	565	0.2132	-	-	-	-	-
9.4333	566	0.4707	-	-	-	-	-
9.45	567	0.3248	-	-	-	-	-
9.4667	568	0.635	-	-	-	-	-
9.4833	569	0.3263	-	-	-	-	-
9.5	570	0.2805	-	-	-	-	-
9.5167	571	0.0421	-	-	-	-	-
9.5333	572	0.4996	-	-	-	-	-
9.55	573	0.2134	-	-	-	-	-
9.5667	574	0.0383	-	-	-	-	-
9.5833	575	0.5026	-	-	-	-	-
9.6	576	0.2033	-	-	-	-	-
9.6167	577	0.147	-	-	-	-	-
9.6333	578	0.381	-	-	-	-	-
9.65	579	0.2251	-	-	-	-	-
9.6667	580	0.2874	-	-	-	-	-
9.6833	581	0.3673	-	-	-	-	-
9.7	582	0.1544	-	-	-	-	-
9.7167	583	0.3899	-	-	-	-	-
9.7333	584	0.3182	-	-	-	-	-
9.75	585	0.3009	-	-	-	-	-
9.7667	586	0.0267	-	-	-	-	-
9.7833	587	0.3682	-	-	-	-	-
9.8	588	0.2009	-	-	-	-	-
9.8167	589	0.1356	-	-	-	-	-
9.8333	590	0.5001	-	-	-	-	-
9.85	591	0.1517	-	-	-	-	-
9.8667	592	0.2848	-	-	-	-	-
9.8833	593	0.3336	-	-	-	-	-
9.9	594	0.2787	-	-	-	-	-
9.9167	595	0.3367	-	-	-	-	-
9.9333	596	0.3952	-	-	-	-	-
9.95	597	0.2262	-	-	-	-	-
9.9667	598	0.355	-	-	-	-	-
9.9833	599	0.4903	-	-	-	-	-
10.0	600	0.0002	0.3435 (+0.1941)	0.6074 (+0.0669)	0.4011 (+0.0760)	0.6901 (+0.1894)	0.5662 (+0.1108)
-1	-1	-	0.3488 (+0.1993)	0.6088 (+0.0683)	0.3953 (+0.0703)	0.6934 (+0.1928)	0.5658 (+0.1105)

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.2
Sentence Transformers: 5.1.0
Transformers: 4.55.0
PyTorch: 2.7.0+cu126
Accelerate: 1.10.0
Datasets: 4.0.0
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}