Update README.md

README.md

@@ -82,22 +82,6 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [B
 - **Language:** en
 - **License:** mit
 
-### 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
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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)
@@ -112,7 +96,6 @@ Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
 
-# Download from the 🤗 Hub
 model = SentenceTransformer("SalmanFaroz/DisEmbed-v1")
 # Run inference
 sentences = [
@@ -130,291 +113,6 @@ print(similarities.shape)
 # [3, 3]
 ```
 
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Dataset
-
-#### Unnamed Dataset
-
-
-* Size: 225,245 training samples
-* Columns: <code>0</code> and <code>1</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | 0                                                                                  | 1                                                                                  |
-  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
-  | type    | string                                                                             | string                                                                             |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 35.19 tokens</li><li>max: 347 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 34.92 tokens</li><li>max: 281 tokens</li></ul> |
-* Samples:
-  | 0                                                                                                                                                 | 1                                                                                                                                                                                                                                                                                                                                                                    |
-  |:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Disease Name : Lymphadenitis, due to, diphtheria</code>                                                                                     | <code>This condition involves lymphadenitis due to diphtheria infection, leading to symptoms such as swelling, tenderness, and potential pain in the lymph nodes. Patients may experience systemic symptoms like fever and malaise, indicating an underlying issue that requires attention. Complications can arise if the condition is not managed properly.</code> |
-  | <code>nephropathy: kidney damage or disease; proteinuria: presence of excess protein in urine; edema: swelling due to fluid retention; ...</code> | <code>Disease Name : Nephropathy, phosphate-losing</code>                                                                                                                                                                                                                                                                                                            |
-  | <code>Disease Name : Cyst, renal</code>                                                                                                           | <code>Renal cysts can lead to symptoms such as flank pain, hematuria, and potential urinary obstruction. If these cysts become infected, they may present with fever, chills, and significant discomfort in the abdominal or back regions.</code>                                                                                                                    |
-* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
-  ```json
-  {
-      "scale": 20.0,
-      "similarity_fct": "cos_sim"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 122
-- `per_device_eval_batch_size`: 122
-- `learning_rate`: 2e-05
-- `num_train_epochs`: 1
-- `warmup_ratio`: 0.1
-- `fp16`: True
-- `batch_sampler`: no_duplicates
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 122
-- `per_device_eval_batch_size`: 122
-- `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`: 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`: 1
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `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`: 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`: False
-- `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
-- `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
-- `eval_on_start`: False
-- `use_liger_kernel`: False
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: no_duplicates
-- `multi_dataset_batch_sampler`: proportional
-
-</details>
-
-### Training Logs
-| Epoch  | Step | Training Loss |
-|:------:|:----:|:-------------:|
-| 0.0541 | 100  | 2.5621        |
-| 0.1083 | 200  | 1.3308        |
-| 0.1624 | 300  | 1.1403        |
-| 0.2166 | 400  | 1.0506        |
-| 0.2707 | 500  | 1.0135        |
-| 0.3249 | 600  | 0.9443        |
-| 0.3790 | 700  | 0.9412        |
-| 0.4331 | 800  | 0.9095        |
-| 0.4873 | 900  | 0.8945        |
-| 0.5414 | 1000 | 0.8533        |
-| 0.5956 | 1100 | 0.8601        |
-| 0.6497 | 1200 | 0.8425        |
-| 0.7038 | 1300 | 0.2919        |
-| 0.7580 | 1400 | 0.0249        |
-| 0.8121 | 1500 | 0.0231        |
-| 0.8663 | 1600 | 0.0182        |
-| 0.9204 | 1700 | 0.0206        |
-| 0.9746 | 1800 | 0.0206        |
-| 0.0541 | 100  | 0.8606        |
-| 0.1083 | 200  | 0.7361        |
-| 0.1624 | 300  | 0.6648        |
-| 0.2166 | 400  | 0.6506        |
-| 0.2707 | 500  | 0.6502        |
-| 0.3249 | 600  | 0.6249        |
-| 0.3790 | 700  | 0.6473        |
-| 0.4331 | 800  | 0.6391        |
-| 0.4873 | 900  | 0.6474        |
-| 0.5414 | 1000 | 0.6316        |
-| 0.5956 | 1100 | 0.6543        |
-| 0.6497 | 1200 | 0.6493        |
-| 0.7038 | 1300 | 0.2173        |
-| 0.7580 | 1400 | 0.0135        |
-| 0.8121 | 1500 | 0.0149        |
-| 0.8663 | 1600 | 0.0128        |
-| 0.9204 | 1700 | 0.0158        |
-| 0.9746 | 1800 | 0.0169        |
-| 0.0541 | 100  | 0.6698        |
-| 0.1083 | 200  | 0.5107        |
-| 0.1624 | 300  | 0.4378        |
-| 0.2166 | 400  | 0.4408        |
-| 0.2707 | 500  | 0.4452        |
-| 0.3249 | 600  | 0.4391        |
-| 0.3790 | 700  | 0.4672        |
-| 0.4331 | 800  | 0.4712        |
-| 0.4873 | 900  | 0.489         |
-| 0.5414 | 1000 | 0.4878        |
-| 0.5956 | 1100 | 0.5196        |
-| 0.6497 | 1200 | 0.5245        |
-| 0.7038 | 1300 | 0.1768        |
-| 0.7580 | 1400 | 0.0091        |
-| 0.8121 | 1500 | 0.0107        |
-| 0.8663 | 1600 | 0.0099        |
-| 0.9204 | 1700 | 0.0127        |
-| 0.9746 | 1800 | 0.0147        |
-| 0.0541 | 100  | 0.5605        |
-| 0.1083 | 200  | 0.3476        |
-| 0.1624 | 300  | 0.2772        |
-| 0.2166 | 400  | 0.2862        |
-| 0.2707 | 500  | 0.2937        |
-| 0.3249 | 600  | 0.2983        |
-| 0.3790 | 700  | 0.3293        |
-| 0.4331 | 800  | 0.3421        |
-| 0.4873 | 900  | 0.3634        |
-| 0.5414 | 1000 | 0.3732        |
-| 0.5956 | 1100 | 0.4125        |
-| 0.6497 | 1200 | 0.4266        |
-| 0.7038 | 1300 | 0.1474        |
-| 0.7580 | 1400 | 0.007         |
-| 0.8121 | 1500 | 0.0081        |
-| 0.8663 | 1600 | 0.0079        |
-| 0.9204 | 1700 | 0.0104        |
-| 0.9746 | 1800 | 0.0132        |
-
-
-### Framework Versions
-- Python: 3.10.12
-- Sentence Transformers: 3.3.1
-- Transformers: 4.47.0
-- PyTorch: 2.1.0+cu118
-- Accelerate: 1.2.1
-- Datasets: 3.2.0
-- Tokenizers: 0.21.0
-
-## Citation
-
-### BibTeX
 
 #### Sentence Transformers
 ```bibtex
@@ -439,22 +137,4 @@ You can finetune this model on your own dataset.
     archivePrefix={arXiv},
     primaryClass={cs.CL}
 }
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+```