First commit of README file

README.md

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----
-license: apache-2.0
----
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2438
+- loss:MatryoshkaLoss
+- loss:OnlineContrastiveLoss
+base_model: Alibaba-NLP/gte-modernbert-base
+
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+- cosine_accuracy_threshold
+- cosine_f1
+- cosine_f1_threshold
+- cosine_precision
+- cosine_recall
+- cosine_ap
+- cosine_mcc
+model-index:
+- name: SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
+  results:
+  - task:
+      type: my-binary-classification
+      name: My Binary Classification
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy
+      value: 0.9159836065573771
+      name: Cosine Accuracy
+    - type: cosine_accuracy_threshold
+      value: 0.8090976476669312
+      name: Cosine Accuracy Threshold
+    - type: cosine_f1
+      value: 0.9216061185468452
+      name: Cosine F1
+    - type: cosine_f1_threshold
+      value: 0.8090976476669312
+      name: Cosine F1 Threshold
+    - type: cosine_precision
+      value: 0.9305019305019305
+      name: Cosine Precision
+    - type: cosine_recall
+      value: 0.9128787878787878
+      name: Cosine Recall
+    - type: cosine_ap
+      value: 0.974188222191262
+      name: Cosine Ap
+    - type: cosine_mcc
+      value: 0.8312925398469787
+      name: Cosine Mcc
+---
+
+# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the csv 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
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision bc02f0a92d1b6dd82108036f6cb4b7b423fb7434 -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - csv
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+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("waris-gill/ModernBert-Medical-v1")
+# Run inference
+sentences = [
+    'My rheumatologist said \'if a patient has lupus then prednisone doesn\'t work." why is that?',
+    "I have lupus,my rheumatologist told me that prednisone doesn't work in my case. Could you educate me why? What are my chances? ",
+    'Hello doctor, my grandmother has 3rd degree bed sore. What can be done to help?',
+]
+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]
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### My Binary Classification
+
+* Evaluated with <code>scache.train.MyBinaryClassificationEvaluator</code>
+
+| Metric                    | Value      |
+|:--------------------------|:-----------|
+| cosine_accuracy           | 0.916      |
+| cosine_accuracy_threshold | 0.8091     |
+| cosine_f1                 | 0.9216     |
+| cosine_f1_threshold       | 0.8091     |
+| cosine_precision          | 0.9305     |
+| cosine_recall             | 0.9129     |
+| **cosine_ap**             | **0.9742** |
+| cosine_mcc                | 0.8313     |
+
+<!--
+## 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
+
+#### csv
+
+* Dataset: csv
+* Size: 2,438 training samples
+* Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "OnlineContrastiveLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### csv
+
+* Dataset: csv
+* Size: 2,438 evaluation samples
+
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 256
+- `learning_rate`: 6.5383156211679e-05
+- `max_grad_norm`: 0.5
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: constant
+- `load_best_model_at_end`: True
+- `torch_compile`: True
+- `torch_compile_backend`: inductor
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 256
+- `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`: 6.5383156211679e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 0.5
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: constant
+- `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`: False
+- `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`: True
+- `torch_compile_backend`: inductor
+- `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 | Validation Loss | cosine_ap  |
+|:----------:|:------:|:-------------:|:---------------:|:----------:|
+| 0.0323     | 1      | 4.4977        | -               | -          |
+| 0.0645     | 2      | 4.9952        | -               | -          |
+| 0.0968     | 3      | 2.9984        | -               | -          |
+| 0.1290     | 4      | 4.8052        | -               | -          |
+| 0.1613     | 5      | 4.0031        | -               | -          |
+| 0.1935     | 6      | 3.7682        | -               | -          |
+| 0.2258     | 7      | 4.0361        | -               | -          |
+| 0.2581     | 8      | 3.4003        | -               | -          |
+| 0.2903     | 9      | 1.1674        | -               | -          |
+| **0.3226** | **10** | **2.3826**    | **14.3756**     | **0.9742** |
+| 0.3548     | 11     | 3.8777        | -               | -          |
+| 0.3871     | 12     | 2.6367        | -               | -          |
+| 0.4194     | 13     | 2.5763        | -               | -          |
+| 0.4516     | 14     | 3.5591        | -               | -          |
+| 0.4839     | 15     | 2.3568        | -               | -          |
+| 0.5161     | 16     | 2.9432        | -               | -          |
+| 0.5484     | 17     | 2.746         | -               | -          |
+| 0.5806     | 18     | 3.647         | -               | -          |
+| 0.6129     | 19     | 3.0907        | -               | -          |
+| 0.6452     | 20     | 3.9776        | 12.4766         | 0.9771     |
+| 0.6774     | 21     | 3.4131        | -               | -          |
+| 0.7097     | 22     | 3.0084        | -               | -          |
+| 0.7419     | 23     | 2.7182        | -               | -          |
+| 0.7742     | 24     | 1.5211        | -               | -          |
+| 0.8065     | 25     | 1.8332        | -               | -          |
+| 0.8387     | 26     | 3.4883        | -               | -          |
+| 0.8710     | 27     | 2.0585        | -               | -          |
+| 0.9032     | 28     | 2.775         | -               | -          |
+| 0.9355     | 29     | 2.9137        | -               | -          |
+| 0.9677     | 30     | 2.4238        | 12.4805         | 0.9769     |
+| 1.0        | 31     | 1.2115        | 14.3756         | 0.9742     |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.49.0
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.4.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+
+
+<!--
+## 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.*
+-->