Update README.md

README.md

@@ -9,14 +9,15 @@ tags:
 - generated_from_trainer
 - dataset_size:31500
 - loss:MatryoshkaLoss
-- loss:CosineSimilarityLoss
 base_model: Ghani-25/LF_enrich_sim
 widget:
 - source_sentence: CTO and co-Founder
   sentences:
   - Responsable surpervision des départements
   - Senior sales executive
-  - Injection Operations Supervisor - Industrial Efficiency - Systems & Equipment
+  - >-
+    Injection Operations Supervisor - Industrial Efficiency - Systems &
+    Equipment
 - source_sentence: Commercial Account Executive
   sentences:
   - Automation Electrician
@@ -114,7 +115,7 @@ model-index:
 
 # Our original base similarity Matryoshka
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Ghani-25/LF_enrich_sim](https://huggingface.co/Ghani-25/LF_enrich_sim) on the json dataset. 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.
+This is a [sentence-transformers] model finetuned from [Ghani-25/LF_enrich_sim](https://huggingface.co/Ghani-25/LF_enrich_sim) on the json dataset. 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
 
@@ -129,12 +130,6 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [G
 - **Language:** multilingual
 - **License:** apache-2.0
 
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
 ### Full Model Architecture
 
 ```
@@ -163,7 +158,7 @@ model = SentenceTransformer("Ghani-25/LF-enrich-sim-matryoshka-64")
 # Run inference
 sentences = [
     'Summer Job: Export Manager',
-    'Responsable Export Afrique Amériques',
+    'Responsable Export Afrique Amériquess
     'Clinical Project Leader',
 ]
 embeddings = model.encode(sentences)
@@ -174,6 +169,11 @@ print(embeddings.shape)
 similarities = model.similarity(embeddings, embeddings)
 print(similarities.shape)
 # [3, 3]
+
+# Extraction de la diagonale pour obtenir les similarités correspondantes
+similarities_diagonal = similarities.diag().cpu().numpy()
+print(similarities_diagonal)
+# [0.896542]
 ```
 
 <!--
@@ -285,119 +285,7 @@ You can finetune this model on your own dataset.
 - `optim`: adamw_torch_fused
 
 #### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: epoch
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 32
-- `per_device_eval_batch_size`: 16
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 16
-- `eval_accumulation_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`: 4
-- `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
-- `use_ipex`: False
-- `bf16`: True
-- `fp16`: False
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: True
-- `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_fused
-- `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`: False
-- `hub_always_push`: False
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `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
-- `dispatch_batches`: None
-- `split_batches`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
+Contact the author.
 
 ### Training Logs
 | Epoch      | Step    | Training Loss | dim_768_spearman_cosine | dim_512_spearman_cosine | dim_256_spearman_cosine | dim_128_spearman_cosine | dim_64_spearman_cosine |
@@ -442,35 +330,6 @@ You can finetune this model on your own dataset.
 - Datasets: 2.19.1
 - Tokenizers: 0.19.1
 
-## 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",
-}
-```
-
-#### MatryoshkaLoss
-```bibtex
-@misc{kusupati2024matryoshka,
-    title={Matryoshka Representation Learning},
-    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
-    year={2024},
-    eprint={2205.13147},
-    archivePrefix={arXiv},
-    primaryClass={cs.LG}
-}
-```
-
 <!--
 ## Glossary