This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct on the mnlp_m3_rag_dataset dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- mnlp_m3_rag_dataset

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    "A builder had a contract to build a swimming pool for a residential customer. That customer's next door neighbor went to the builder and paid him extra to break the contract with the customer and instead to build a swimming pool on the neighbor's premises. The builder commenced building a swimming pool for the neighbor and breached his contract with the original customer. The original customer sued his neighbor in a tort claim for damages. Does the original customer have a valid claim against his neighbor?\nA. Yes, the neighbor committed the tort of interference with contract relations by intentionally interfering with an existing contract.\nB. No, people cannot be held in slavery\nC. they have the right to contract with whomever they please.\nD. No, the only remedy for the original customer is to sue the builder for breach of contract.\nE. Yes, the neighbor committed the tort of interference with prospective advantage.",
    'A tort is a civil wrong that causes harm or loss to another person, resulting in legal liability for the person who commits the tort. Tort law allows individuals to seek compensation for injuries or damages caused by the wrongful acts of others, distinct from breaches of contract.',
    'Substance use, such as alcohol and tobacco, during pregnancy can lead to various complications including low birth weight, developmental issues, and increased risk of infections, highlighting the importance of cessation and support for affected mothers.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

mnlp_m3_rag_dataset

Dataset: mnlp_m3_rag_dataset at e16d937
Size: 594,028 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 21 tokens
mean: 359.4 tokens
max: 512 tokens
min: 4 tokens
mean: 56.63 tokens
max: 433 tokens

Samples:

anchor	positive
Little Lopsy fluttered into our home and our hearts one Saturday morning this summer. My husband went out to do something, and when he opened the door there was a great flutter on the ground and something came into the living room. It was clear that whatever it was was hurt. I was in a bit of a shock and didn't know what to do next. Fortunately it calmed down and tried to hide itself in a corner. I realized it was a sparrow chick . There are a few sparrow nests under the roof of our apartment, and this little fellow must have fallen out and hurt itself. It was also very young, and obviously far from ready to leave the safety of the nest. I ran to the place and found a box. Having read somewhere that one shouldn't touch a baby bird with one's hands, I picked the chick up with a hand towel and put it in the box. I placed the box outside the front door in the hope that the parents would try to feed it. They never came near it and I brought it inside. I placed the box on a table and it sl...	`Having read somewhere that one shouldn't touch a baby bird with one's hands, I picked the chick up with a hand towel and put it in the box.`
`A thermal conductor is made of[object Object]A. types of rubber[object Object]B. types of wire[object Object]C. electrodes[object Object]D. that which conducts`	`A thermal conductor is a material that allows heat to flow through it easily. Common examples of thermal conductors include metals such as copper and aluminum, known for their high thermal conductivity due to their free-flowing electrons. Heat transfer occurs via conduction when heat energy moves from the hotter part of a conductor to the cooler part, often described by Fourier's Law of heat conduction.`
`A good example of increased demand may equal increased production is[object Object]A. soldiers eat beans, so beans are planted when there is war[object Object]B. dogs eat kibble, so stores sell it[object Object]C. cats eat mice, so mice are afraid of cats[object Object]D. people have babies, so baby clothes are made`	`Supply is the total amount of a specific good or service that is available to consumers. Supply can relate to the amount available at a specific price or the amount available across a range of prices if displayed on a graph.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

mnlp_m3_rag_dataset

Dataset: mnlp_m3_rag_dataset at e16d937
Size: 5,920 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 22 tokens
mean: 98.74 tokens
max: 512 tokens
min: 9 tokens
mean: 59.88 tokens
max: 501 tokens

Samples:

anchor	positive
`ക്രൂരകോഷ്ഠം ഉള്ള ഒരാളിൽ കോപിച്ചിരിക്കുന്ന ദോഷം താഴെപ്പറയുന്നവയിൽ ഏതാണ്?[object Object]A. കഫം[object Object]B. പിത്തം[object Object]C. വാതം[object Object]D. രക്തം`	`ഓരോ ദോഷത്തിനും അതിന്റേതായ സ്വഭാവങ്ങളും ശരീരത്തിൽ അത് ഉണ്ടാക്കുന്ന ഫലങ്ങളും ഉണ്ട്.`
`Melyik tényező nem befolyásolja a fagylalt keresleti függvényét?[object Object]A. A fagylalt árának változása.[object Object]B. Mindegyik tényező befolyásolja.[object Object]C. A jégkrém árának változása.[object Object]D. A fagylalttölcsér árának változása.`	`A keresleti függvény negatív meredekségű, ami azt jelenti, hogy az ár növekedésével a keresett mennyiség csökken (csökkenő kereslet törvénye).`
In contrast to _______, _______ aim to reward favourable behaviour by companies. The success of such campaigns have been heightened through the use of ___________, which allow campaigns to facilitate the company in achieving _________ .[object Object]A. Boycotts, Buyalls, Blockchain technology, Increased Sales[object Object]B. Buycotts, Boycotts, Digital technology, Decreased Sales[object Object]C. Boycotts, Buycotts, Digital technology, Decreased Sales[object Object]D. Buycotts, Boycotts, Blockchain technology, Charitable donations[object Object]E. Boycotts, Buyalls, Blockchain technology, Charitable donations[object Object]F. Boycotts, Buycotts, Digital technology, Increased Sales[object Object]G. Buycotts, Boycotts, Digital technology, Increased Sales[object Object]H. Boycotts, Buycotts, Physical technology, Increased Sales[object Object]I. Buycotts, Buyalls, Blockchain technology, Charitable donations[object Object]J. Boycotts, Buycotts, Blockchain technology, Decreased Sales	`[object Object]: This term refers to the actions taken by consumers to promote social, political, or environmental causes. These actions can include boycotting certain companies or buycotting others, influencing market dynamics based on ethical considerations. The effectiveness of consumer activism can vary but has gained prominence in recent years with increased visibility through social media.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 2
learning_rate: 2e-05
warmup_steps: 5569
fp16: True
load_best_model_at_end: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 5569
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: True
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: 0
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
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
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.15	2785	0.2684	0.0411
0.3001	5570	0.1112	0.0541
0.4501	8355	0.1153	0.0633
0.6001	11140	0.1045	0.0582
0.7501	13925	0.0943	0.0606
0.9002	16710	0.0883	0.0563
1.0502	19495	0.0744	0.0505
1.2002	22280	0.0592	0.0523
1.3502	25065	0.059	0.0516
1.5002	27850	0.0544	0.0617
1.6503	30635	0.0521	0.0549
1.8003	33420	0.0502	0.0589
1.9503	36205	0.0449	0.0550
2.1003	38990	0.0369	0.0619
2.2503	41775	0.0331	0.0604
2.4004	44560	0.0308	0.0566
2.5504	47345	0.0294	0.0533
2.7004	50130	0.0286	0.0531
2.8504	52915	0.0266	0.0537

The bold row denotes the saved checkpoint. The training took 6h52m on a RTX5090

Framework Versions

Python: 3.12.3
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.7.0+cu128
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 21 tokens mean: 359.4 tokens max: 512 tokens	min: 4 tokens mean: 56.63 tokens max: 433 tokens

DoDucAnh/MNLP_M3_document_encoder

SentenceTransformer based on intfloat/multilingual-e5-large-instruct

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

mnlp_m3_rag_dataset

Evaluation Dataset

mnlp_m3_rag_dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

DoDucAnh/
MNLP_M3_document_encoder