This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base. It maps sentences & paragraphs to a 768-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-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 768, '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 = [
    "Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: bot_0: Hi, I love country music. Do you have a favorite type of music?\nbot_1: I like classic country music, like patsy cline and hank snow.\nbot_0: That's awesome. I live in the country so it fits me, where do you live?\nbot_1: Up in alpine county ca. It's lovely up here in early spring.\nbot_0: I like spring, but winter is my favorite time of year.\nbot_1: I am so done with winter. Tough getting the kids to school. No snow days.\nbot_0: I understand, we live on a corn and bean farm and it can be hard.\nbot_1: I love farm-fresh organic food. It's all I make.\nbot_0: That's nice. I love living on the farm and driving the tractors.\nbot_1: I could do that! I would love farm life, but we live in the mountains.\nbot_0: That would be amazing! Have you lived there your entire life?\nbot_1: Yes, born and raised here in the sierras. We ski and snowmobile.\nbot_0: Amazing, were your parents from there also? My father is a preacher.",
    'Followup phrase: My great-grandpa came to the Sierras in 1903. My great-grandpa came to mine gold.',
    'Followup phrase: I read stealth and espionage themed books.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Binary Classification

Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.8978
cosine_accuracy_threshold	0.772
cosine_f1	0.6527
cosine_f1_threshold	0.7546
cosine_precision	0.6602
cosine_recall	0.6453
cosine_ap	0.7302
cosine_mcc	0.5842

Training Details

Training Dataset

Unnamed Dataset

Size: 98,646 training samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 1000 samples:
sentence1 sentence2
type string string
details
min: 35 tokens
mean: 143.84 tokens
max: 319 tokens
min: 9 tokens
mean: 15.99 tokens
max: 36 tokens

Samples:

sentence1	sentence2
`Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context[object Object]Dialogue Context: bot_0: What kind of car do you own? I have a jeep.`	`Followup phrase: I live semi-close towkr. I don't own a car. I enjoy running and walking. I live in a small town.`
`Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context[object Object]Dialogue Context: bot_0: What kind of car do you own? I have a jeep.[object Object]bot_1: I don't own my own car! I actually really enjoying walking and running, but then again, I live in a small town and semi-close to work.`	`Followup phrase: I enjoy exercising at the gym.`
`Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context[object Object]Dialogue Context: bot_0: What kind of car do you own? I have a jeep.[object Object]bot_1: I don't own my own car! I actually really enjoying walking and running, but then again, I live in a small town and semi-close to work.[object Object]bot_0: Ah I see! I like going to the gym to work out.`	`Followup phrase: I'm a computer programmer.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 100, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 67,188 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 39 tokens
mean: 146.95 tokens
max: 294 tokens
min: 10 tokens
mean: 18.06 tokens
max: 74 tokens
0: ~83.30%
1: ~16.70%

Samples:

sentence1	sentence2	label
`Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context[object Object]Dialogue Context: bot_0: Hi, I'm shelby, nice to meet you!`	`Followup phrase: Name is Jessica.`	`1`
`Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context[object Object]Dialogue Context: bot_0: Hi, I'm shelby, nice to meet you!`	`Followup phrase: I think fast food is dangerous.`	`0`
`Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context[object Object]Dialogue Context: bot_0: Hi, I'm shelby, nice to meet you!`	`Followup phrase: I also have many allergies. I get hay fever and it makes me sneeze.`	`0`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 100, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 100
per_device_eval_batch_size: 100
weight_decay: 0.01
num_train_epochs: 5
bf16: True
load_best_model_at_end: True
prompts: {'sentence1': 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: ', 'sentence2': 'Followup phrase: '}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 100
per_device_eval_batch_size: 100
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: 5e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: True
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: 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: {'sentence1': 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: ', 'sentence2': 'Followup phrase: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_ap
0.1013	100	2.4265	-	-
0.2026	200	2.1867	-	-
0.3040	300	2.1658	-	-
0.4053	400	2.0992	-	-
0.5066	500	2.0569	-	-
0.6079	600	2.0444	-	-
0.7092	700	2.0274	-	-
0.8105	800	2.0095	-	-
0.9119	900	2.0072	-	-
1.0	987	-	6.0145	0.7222
1.0132	1000	1.9768	-	-
1.1145	1100	1.932	-	-
1.2158	1200	1.9352	-	-
1.3171	1300	1.8966	-	-
1.4184	1400	1.9461	-	-
1.5198	1500	1.8999	-	-
1.6211	1600	1.9098	-	-
1.7224	1700	1.9049	-	-
1.8237	1800	1.9113	-	-
1.9250	1900	1.9211	-	-
2.0	1974	-	6.2014	0.7302

Framework Versions

Python: 3.10.18
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.7.1+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}
}

	sentence1	sentence2
type	string	string
details	min: 35 tokens mean: 143.84 tokens max: 319 tokens	min: 9 tokens mean: 15.99 tokens max: 36 tokens

adugeen/facts-reranker-e5-base

SentenceTransformer based on intfloat/multilingual-e5-base

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Binary Classification

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

adugeen/
facts-reranker-e5-base