Add README file (#1)

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	---
	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]
	```

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	### Direct Usage (Transformers)

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	</details>
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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

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	## 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 \|

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	## 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.
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	## 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",
	}
	```



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