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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-cross_entropy
-  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-cross_entropy
 
-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
+# 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-cross_entropy")
 
-## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| 1.1754        | 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 |
+)
 
+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

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 version https://git-lfs.github.com/spec/v1
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+oid sha256:14366241e1364deebf5c375c922c6b0525d3c57c58f209c415baad4e97cc8ed3
 size 438038894