Model save

README.md

@@ -1,57 +1,58 @@
 ---
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
+license: mit
+base_model: BAAI/bge-large-en-v1.5
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-
+- generated_from_trainer
+model-index:
+- name: PMC_bge
+  results: []
 ---
 
-# {MODEL_NAME}
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-## Usage (Sentence-Transformers)
+# PMC_bge
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on an unknown dataset.
 
-```
-pip install -U sentence-transformers
-```
+## Model description
 
-Then you can use the model like this:
+More information needed
 
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+## Intended uses & limitations
 
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
+More information needed
 
+## Training and evaluation data
 
+More information needed
 
-## Evaluation Results
+## Training procedure
 
-<!--- Describe how your model was evaluated -->
+### Training hyperparameters
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 512
+- total_eval_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20.0
 
+### Training results
 
 
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, '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()
-)
-```
 
-## Citing & Authors
+### Framework versions
 
-<!--- Describe where people can find more information -->
+- Transformers 4.40.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "./output_final_bge/checkpoint-2400",
+  "_name_or_path": "./output_final_bge",
   "architectures": [
     "BertModel"
   ],

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:1bc72b6de20dc182ddd5d911e6d8567c128efa9c45391afb22716395d7ba9347
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