rishavranaut/Qwen2_7B_Task2_semantic_pred

README.md

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+---
+base_model: Qwen/Qwen2-7B
+library_name: peft
+license: apache-2.0
+metrics:
+- accuracy
+- precision
+- recall
+tags:
+- generated_from_trainer
+model-index:
+- name: Qwen2_7B_Task2_semantic_pred
+  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. -->
+
+# Qwen2_7B_Task2_semantic_pred
+
+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: 0.5963
+- Accuracy: 0.8123
+- Precision: 0.8123
+- Recall: 0.8123
+- F1 score: 0.8123
+
+## 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: 16
+- 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 0.6405        | 0.5208 | 200  | 0.5790          | 0.7666   | 0.7666    | 0.7666 | 0.7666   |
+| 0.4689        | 1.0417 | 400  | 0.9852          | 0.6649   | 0.6649    | 0.6649 | 0.6649   |
+| 0.3635        | 1.5625 | 600  | 0.4249          | 0.8188   | 0.8188    | 0.8188 | 0.8188   |
+| 0.3197        | 2.0833 | 800  | 0.7777          | 0.7353   | 0.7353    | 0.7353 | 0.7353   |
+| 0.267         | 2.6042 | 1000 | 0.7223          | 0.7679   | 0.7679    | 0.7679 | 0.7679   |
+| 0.2272        | 3.125  | 1200 | 0.4841          | 0.8201   | 0.8201    | 0.8201 | 0.8201   |
+| 0.1848        | 3.6458 | 1400 | 0.4985          | 0.8227   | 0.8227    | 0.8227 | 0.8227   |
+| 0.1744        | 4.1667 | 1600 | 0.6254          | 0.8044   | 0.8044    | 0.8044 | 0.8044   |
+| 0.1402        | 4.6875 | 1800 | 0.5963          | 0.8123   | 0.8123    | 0.8123 | 0.8123   |
+
+
+### Framework versions
+
+- PEFT 0.12.0
+- Transformers 4.44.2
+- Pytorch 2.3.0
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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