Model save

README.md

@@ -1,37 +1,72 @@
 ---
-language: en
+library_name: transformers
 tags:
-- bert
-- classification
-- pytorch
-pipeline_tag: text-classification
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bert-clf-biencoder-cross_entropy
+  results: []
 ---
 
-# BiEncoder Classification Model
+<!-- 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. -->
 
-This model is a BiEncoder architecture based on BERT for text pair classification.
+# bert-clf-biencoder-cross_entropy
 
-## Model Details
-- Base Model: bert-base-uncased
-- Architecture: BiEncoder with BERT base
-- Number of classes: 4
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9632
+- Accuracy: 0.6667
+- F1: 0.6671
+- Precision: 0.6693
+- Recall: 0.6667
 
-## Usage
+## Model description
 
-```python
-from transformers import AutoTokenizer
-import torch
+More information needed
 
-# Load tokenizer
-tokenizer = AutoTokenizer.from_pretrained("minoosh/bert-clf-biencoder-cross_entropy")
+## Intended uses & limitations
 
-# Load model weights
-state_dict = torch.load("pytorch_model.bin")
+More information needed
 
-# Initialize model (you'll need the BiEncoderModel class)
-model = BiEncoderModel(
-    base_model=AutoModel.from_pretrained("bert-base-uncased"),
-    num_classes=4
-)
-model.load_state_dict(state_dict)
-```
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 7
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 1.1754        | 1.0   | 78   | 1.0595          | 0.5955   | 0.5784 | 0.5956    | 0.5955 |
+| 0.8707        | 2.0   | 156  | 0.8633          | 0.6505   | 0.6425 | 0.6649    | 0.6505 |
+| 0.6367        | 3.0   | 234  | 0.8300          | 0.6893   | 0.6939 | 0.7081    | 0.6893 |
+| 0.5392        | 4.0   | 312  | 0.8422          | 0.6893   | 0.6903 | 0.6971    | 0.6893 |
+| 0.3485        | 5.0   | 390  | 0.8752          | 0.6893   | 0.6898 | 0.6924    | 0.6893 |
+| 0.2629        | 6.0   | 468  | 0.9302          | 0.6796   | 0.6797 | 0.6800    | 0.6796 |
+| 0.1981        | 7.0   | 546  | 0.9632          | 0.6667   | 0.6671 | 0.6693    | 0.6667 |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.0
+- Datasets 3.0.1
+- Tokenizers 0.20.0

model.safetensors

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