This is a sentence-transformers model finetuned from almanach/camembert-bio-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: almanach/camembert-bio-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, 'architecture': 'CamembertModel'})
  (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})
)

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 = [
    'carcinome',
    '8000/6 Métastase, SAI',
    '9800/3 Leucémie subaiguë, SAI [obs]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5178, -0.0998],
#         [ 0.5178,  1.0000,  0.0030],
#         [-0.0998,  0.0030,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 8,434 training samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 1000 samples:
sentence1 sentence2
type string string
details
min: 3 tokens
mean: 11.31 tokens
max: 43 tokens
min: 6 tokens
mean: 15.69 tokens
max: 34 tokens

Samples:

sentence1	sentence2
`tumeur à petites cellules rondes`	`8806/3 Tumeur desmoplastique à petites cellules rondes`
`dissémination oligométastatique`	`8000/6 Néoplasme métastatique`
`processus néoprolifératif primaire`	`8000/3 Tumeur maligne non classée`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

Unnamed Dataset

Size: 444 evaluation samples
Columns: sentence1 and sentence2
Approximate statistics based on the first 444 samples:
sentence1 sentence2
type string string
details
min: 3 tokens
mean: 11.68 tokens
max: 31 tokens
min: 6 tokens
mean: 15.76 tokens
max: 34 tokens
Samples:
sentence1 sentence2
neuroblastome 9500/3 Neuroblastome, SAI
Sarcome méningothélial 9530/3 Sarcome méningothélial
Carcinome excréto-urinaire, SAI 8120/3 Carcinome urothélial, SAI
Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 64
gradient_accumulation_steps: 2
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 30
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
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: 16
per_device_eval_batch_size: 64
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: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 30
max_steps: -1
lr_scheduler_type: cosine
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.9434	50	3.4933	-
1.0	53	-	2.1337
1.8868	100	2.2572	-
2.0	106	-	1.7004
2.8302	150	1.6176	-
3.0	159	-	1.4333
3.7736	200	1.2587	-
4.0	212	-	1.2576
4.7170	250	1.0359	-
5.0	265	-	1.1235
5.6604	300	0.9043	-
6.0	318	-	1.0555
6.6038	350	0.8331	-
7.0	371	-	0.9720
7.5472	400	0.7715	-
8.0	424	-	0.9189
8.4906	450	0.7566	-
9.0	477	-	0.8910
9.4340	500	0.7205	-
10.0	530	-	0.8724
10.3774	550	0.7056	-
11.0	583	-	0.8717
11.3208	600	0.6927	-
12.0	636	-	0.8567
12.2642	650	0.6798	-
13.0	689	-	0.8519
13.2075	700	0.6689	-
14.0	742	-	0.8806
14.1509	750	0.6643	-
15.0	795	-	0.8405
15.0943	800	0.6622	-
16.0	848	-	0.8529
16.0377	850	0.6546	-
16.9811	900	0.6358	-
17.0	901	-	0.8474

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.14
Sentence Transformers: 5.1.2
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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: 3 tokens mean: 11.31 tokens max: 43 tokens	min: 6 tokens mean: 15.69 tokens max: 34 tokens

sentence1	sentence2
`neuroblastome`	`9500/3 Neuroblastome, SAI`
`Sarcome méningothélial`	`9530/3 Sarcome méningothélial`
`Carcinome excréto-urinaire, SAI`	`8120/3 Carcinome urothélial, SAI`

Mehd212/camembert-bio-morpho-bi-encoder

SentenceTransformer based on almanach/camembert-bio-base

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

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

Mehd212/
camembert-bio-morpho-bi-encoder