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README.md

@@ -1,72 +1,74 @@
 ---
-library_name: transformers
+
+language: en
+
 tags:
-- generated_from_trainer
-metrics:
-- accuracy
-- f1
-- precision
-- recall
-model-index:
-- name: bert-clf-biencoder-kl_divergence
-  results: []
+
+- bert
+
+- classification
+
+- pytorch
+
+pipeline_tag: text-classification
+
 ---
 
-<!-- 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. -->
 
-# bert-clf-biencoder-kl_divergence
 
-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: 0.9469
-- Accuracy: 0.6828
-- F1: 0.6844
-- Precision: 0.6937
-- Recall: 0.6828
+# BiEncoder Classification Model
+
+
+
+This model is a BiEncoder architecture based on BERT for text pair classification.
+
+
+
+## Model Details
+
+- Base Model: bert-base-uncased
+
+- Architecture: BiEncoder with BERT base
+
+- Number of classes: 4
+
+
+
+## Usage
+
+
+
+```python
+
+from transformers import AutoTokenizer
+
+import torch
+
+
+
+# Load tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("minoosh/bert-clf-biencoder-kl_divergence")
 
-## Model description
 
-More information needed
 
-## Intended uses & limitations
+# Load model weights
 
-More information needed
+state_dict = torch.load("pytorch_model.bin")
 
-## Training and evaluation data
 
-More information needed
 
-## Training procedure
+# Initialize model (you'll need the BiEncoderModel class)
 
-### Training hyperparameters
+model = BiEncoderModel(
 
-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
+    base_model=AutoModel.from_pretrained("bert-base-uncased"),
 
-### Training results
+    num_classes=4
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 1.1449        | 1.0   | 78   | 1.0565          | 0.5987   | 0.5820 | 0.6068    | 0.5987 |
-| 0.8672        | 2.0   | 156  | 0.8471          | 0.6505   | 0.6449 | 0.6611    | 0.6505 |
-| 0.6288        | 3.0   | 234  | 0.8003          | 0.6828   | 0.6858 | 0.6933    | 0.6828 |
-| 0.5023        | 4.0   | 312  | 0.8179          | 0.6893   | 0.6911 | 0.7008    | 0.6893 |
-| 0.332         | 5.0   | 390  | 0.8610          | 0.6861   | 0.6866 | 0.6907    | 0.6861 |
-| 0.2637        | 6.0   | 468  | 0.9075          | 0.6861   | 0.6870 | 0.6890    | 0.6861 |
-| 0.1895        | 7.0   | 546  | 0.9469          | 0.6828   | 0.6844 | 0.6937    | 0.6828 |
+)
 
+model.load_state_dict(state_dict)
 
-### Framework versions
+```
 
-- Transformers 4.45.1
-- Pytorch 2.4.0
-- Datasets 3.0.1
-- Tokenizers 0.20.0

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:e3d749d79c8e440803ab13eb40191813a36da178c900fdfbab8ca81b071b7447
 size 438038894