SentenceTransformer based on intfloat/multilingual-e5-base
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.
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
andsentence2
- 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
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
Dialogue Context: bot_0: What kind of car do you own? I have a jeep.
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
Dialogue Context: bot_0: What kind of car do you own? I have a jeep.
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.
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:{ "scale": 100, "similarity_fct": "cos_sim" }
Evaluation Dataset
Unnamed Dataset
- Size: 67,188 evaluation samples
- Columns:
sentence1
,sentence2
, andlabel
- 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
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
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
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:{ "scale": 100, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 100per_device_eval_batch_size
: 100weight_decay
: 0.01num_train_epochs
: 5bf16
: Trueload_best_model_at_end
: Trueprompts
: {'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
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 100per_device_eval_batch_size
: 100per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.01adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: {'sentence1': 'Instruct: Given a dialogue context, retrieve relevant followup phrase that align with the context\nDialogue Context: ', 'sentence2': 'Followup phrase: '}batch_sampler
: no_duplicatesmulti_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
@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
@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}
}
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Base model
intfloat/multilingual-e5-baseEvaluation results
- Cosine Accuracy on Unknownself-reported0.898
- Cosine Accuracy Threshold on Unknownself-reported0.772
- Cosine F1 on Unknownself-reported0.653
- Cosine F1 Threshold on Unknownself-reported0.755
- Cosine Precision on Unknownself-reported0.660
- Cosine Recall on Unknownself-reported0.645
- Cosine Ap on Unknownself-reported0.730
- Cosine Mcc on Unknownself-reported0.584