Model save

README.md

@@ -1,57 +1,68 @@
 ---
-language: en
+library_name: transformers
 tags:
-- bert
-- regression
-- biencoder
-- similarity
-pipeline_tag: text-similarity
+- generated_from_trainer
+model-index:
+- name: bert-reg-biencoder-mae
+  results: []
 ---
 
-# BiEncoder Regression Model
-
-This model is a BiEncoder architecture that outputs similarity scores between text pairs.
-
-## Model Details
-- Base Model: bert-base-uncased
-- Task: Regression
-- Architecture: BiEncoder with cosine similarity
-- Loss Function: mae
-
-## Usage
-
-```python
-from transformers import AutoTokenizer, AutoModel
-from modeling import BiEncoderModelRegression
-
-# Load model components
-tokenizer = AutoTokenizer.from_pretrained("minoosh/bert-reg-biencoder-mae")
-base_model = AutoModel.from_pretrained("bert-base-uncased")
-model = BiEncoderModelRegression(base_model, loss_fn="mae")
-
-# Load weights
-state_dict = torch.load("pytorch_model.bin")
-model.load_state_dict(state_dict)
-
-# Prepare inputs
-texts1 = ["first text"]
-texts2 = ["second text"]
-inputs = tokenizer(
-    texts1, texts2,
-    padding=True,
-    truncation=True,
-    return_tensors="pt"
-)
-
-# Get similarity scores
-outputs = model(**inputs)
-similarity_scores = outputs["logits"]
-```
-
-## Metrics
-The model was trained using mae loss and evaluated using:
-- Mean Squared Error (MSE)
-- Mean Absolute Error (MAE)
-- Pearson Correlation
-- Spearman Correlation
-- Cosine Similarity
+<!-- 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-reg-biencoder-mae
+
+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: 0.2340
+- Mse: 0.0819
+- Mae: 0.2335
+- Pearson Corr: 0.2475
+- Spearman Corr: 0.1329
+- Cosine Sim: 0.9022
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## 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 | Mse    | Mae    | Pearson Corr | Spearman Corr | Cosine Sim |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------------:|:-------------:|:----------:|
+| 0.2846        | 1.0   | 21   | 0.2617          | 0.1153 | 0.2610 | 0.1327       | 0.0936        | 0.9053     |
+| 0.2728        | 2.0   | 42   | 0.2310          | 0.0886 | 0.2304 | 0.0188       | 0.0316        | 0.8994     |
+| 0.2511        | 3.0   | 63   | 0.2282          | 0.0847 | 0.2276 | 0.1716       | 0.1111        | 0.9058     |
+| 0.2253        | 4.0   | 84   | 0.2333          | 0.0864 | 0.2329 | 0.1906       | 0.1191        | 0.9041     |
+| 0.1993        | 5.0   | 105  | 0.2329          | 0.0822 | 0.2325 | 0.2303       | 0.1246        | 0.9016     |
+| 0.1844        | 6.0   | 126  | 0.2357          | 0.0828 | 0.2352 | 0.2284       | 0.1254        | 0.9018     |
+| 0.165         | 7.0   | 147  | 0.2340          | 0.0819 | 0.2335 | 0.2475       | 0.1329        | 0.9022     |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.0
+- Datasets 3.0.1
+- Tokenizers 0.20.0

model.safetensors

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