rishavranaut/QWEN_2_7B_Fact_U

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_Fact_U
+  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_Fact_U
+
+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: 1.0739
+- Accuracy: 0.7812
+- Precision: 0.8033
+- Recall: 0.7670
+- F1 score: 0.7847
+
+## 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: 4
+- 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 | Accuracy | F1 score | Precision | Recall | Validation Loss |
+|:-------------:|:------:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:|
+| 0.9778        | 0.2509 | 200  | 0.6529   | 0.7128   | 0.6256    | 0.8281 | 0.8856          |
+| 0.8085        | 0.5019 | 400  | 0.7376   | 0.7514   | 0.7407    | 0.7624 | 0.7247          |
+| 0.728         | 0.7528 | 600  | 0.7541   | 0.7628   | 0.7654    | 0.7602 | 0.6549          |
+| 0.6569        | 1.0038 | 800  | 0.6584   | 0.7671   | 0.7891    | 0.7534 | 0.7708          |
+| 0.4831        | 1.2547 | 1000 | 0.7161   | 0.7118   | 0.6756    | 0.8575 | 0.7557          |
+| 0.4952        | 1.5056 | 1200 | 0.6699   | 0.7788   | 0.7848    | 0.7919 | 0.7883          |
+| 0.4992        | 1.7566 | 1400 | 0.6185   | 0.7824   | 0.8173    | 0.7489 | 0.7816          |
+| 0.4104        | 2.0075 | 1600 | 0.6325   | 0.7753   | 0.7770    | 0.7964 | 0.7866          |
+| 0.3071        | 2.2585 | 1800 | 0.6979   | 0.7635   | 0.7796    | 0.7602 | 0.7698          |
+| 0.3557        | 2.5094 | 2000 | 0.7270   | 0.7694   | 0.7617    | 0.8100 | 0.7851          |
+| 0.3088        | 2.7604 | 2200 | 0.7186   | 0.7788   | 0.8024    | 0.7624 | 0.7819          |
+| 0.3848        | 3.0113 | 2400 | 0.6926   | 0.7718   | 0.7857    | 0.7715 | 0.7785          |
+| 0.2186        | 3.2622 | 2600 | 0.7965   | 0.7835   | 0.8225    | 0.7443 | 0.7815          |
+| 0.2531        | 3.5132 | 2800 | 0.7843   | 0.78     | 0.8364    | 0.7172 | 0.7722          |
+| 0.2134        | 3.7641 | 3000 | 0.7977   | 0.7729   | 0.7849    | 0.7760 | 0.7804          |
+| 0.1961        | 4.0151 | 3200 | 0.8438   | 0.7871   | 0.8443    | 0.7240 | 0.7795          |
+| 0.1464        | 4.2660 | 3400 | 0.9256   | 0.7835   | 0.8225    | 0.7443 | 0.7815          |
+| 0.1132        | 4.5169 | 3600 | 1.0431   | 0.7824   | 0.8052    | 0.7670 | 0.7856          |
+| 0.1323        | 4.7679 | 3800 | 1.0739   | 0.7812   | 0.8033    | 0.7670 | 0.7847          |
+
+
+### 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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