rishavranaut/QWEN_2_7B_MT

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: Qwen/Qwen2-7B
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: QWEN_2_7B_MT
+  results: []
+---
+
+<!-- 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. -->
+
+# QWEN_2_7B_MT
+
+This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7702
+- Accuracy: 0.78
+- Precision: 0.7979
+- Recall: 0.75
+- F1 score: 0.7732
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 0.93          | 0.25  | 200  | 0.8818          | 0.6517   | 0.7264    | 0.4867 | 0.5828   |
+| 0.6783        | 0.5   | 400  | 1.1545          | 0.59     | 0.7411    | 0.2767 | 0.4029   |
+| 0.6849        | 0.75  | 600  | 0.7826          | 0.685    | 0.7424    | 0.5667 | 0.6427   |
+| 0.6378        | 1.0   | 800  | 0.7399          | 0.695    | 0.7647    | 0.5633 | 0.6488   |
+| 0.4773        | 1.25  | 1000 | 0.9978          | 0.66     | 0.8077    | 0.42   | 0.5526   |
+| 0.4842        | 1.5   | 1200 | 0.6576          | 0.7333   | 0.6902    | 0.8467 | 0.7605   |
+| 0.456         | 1.75  | 1400 | 0.6290          | 0.77     | 0.8068    | 0.71   | 0.7553   |
+| 0.464         | 2.0   | 1600 | 0.6295          | 0.7617   | 0.7774    | 0.7333 | 0.7547   |
+| 0.3689        | 2.25  | 1800 | 0.6090          | 0.7783   | 0.8224    | 0.71   | 0.7621   |
+| 0.307         | 2.5   | 2000 | 0.6058          | 0.7733   | 0.7808    | 0.76   | 0.7703   |
+| 0.3302        | 2.75  | 2200 | 0.6024          | 0.7867   | 0.7810    | 0.7967 | 0.7888   |
+| 0.3156        | 3.0   | 2400 | 0.7082          | 0.765    | 0.8272    | 0.67   | 0.7403   |
+| 0.2571        | 3.25  | 2600 | 0.9494          | 0.7017   | 0.8168    | 0.52   | 0.6354   |
+| 0.2593        | 3.5   | 2800 | 0.6498          | 0.7717   | 0.8030    | 0.72   | 0.7592   |
+| 0.2458        | 3.75  | 3000 | 0.6434          | 0.7867   | 0.7810    | 0.7967 | 0.7888   |
+| 0.2135        | 4.0   | 3200 | 0.7042          | 0.775    | 0.7978    | 0.7367 | 0.7660   |
+| 0.1582        | 4.25  | 3400 | 0.6730          | 0.78     | 0.7530    | 0.8333 | 0.7911   |
+| 0.158         | 4.5   | 3600 | 0.7538          | 0.7783   | 0.8104    | 0.7267 | 0.7663   |
+| 0.2043        | 4.75  | 3800 | 0.7398          | 0.785    | 0.8065    | 0.75   | 0.7772   |
+| 0.163         | 5.0   | 4000 | 0.7702          | 0.78     | 0.7979    | 0.75   | 0.7732   |
+
+
+### Framework versions
+
+- PEFT 0.11.1
+- Transformers 4.44.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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