ikura31/mistral_docs_sum_p1_r0

README.md

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+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.1
+tags:
+- generated_from_trainer
+model-index:
+- name: mistral_docs_sum_p1_full
+  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. -->
+
+# mistral_docs_sum_p1_full
+
+This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5829
+
+## 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: 3.6e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 2.1167        | 0.0277 | 200  | 2.1333          |
+| 2.3428        | 0.0553 | 400  | 1.6966          |
+| 1.3784        | 0.0830 | 600  | 1.4972          |
+| 1.456         | 0.1107 | 800  | 1.3942          |
+| 1.3227        | 0.1383 | 1000 | 1.3084          |
+| 1.2535        | 0.1660 | 1200 | 1.2001          |
+| 1.0612        | 0.1937 | 1400 | 1.0451          |
+| 0.8815        | 0.2213 | 1600 | 0.9632          |
+| 0.8971        | 0.2490 | 1800 | 0.9132          |
+| 0.7908        | 0.2767 | 2000 | 0.8712          |
+| 0.7549        | 0.3043 | 2200 | 0.8309          |
+| 0.8099        | 0.3320 | 2400 | 0.8058          |
+| 0.6891        | 0.3597 | 2600 | 0.7879          |
+| 0.5204        | 0.3873 | 2800 | 0.7684          |
+| 0.6249        | 0.4150 | 3000 | 0.7515          |
+| 0.6764        | 0.4427 | 3200 | 0.7342          |
+| 0.6996        | 0.4703 | 3400 | 0.7214          |
+| 0.6371        | 0.4980 | 3600 | 0.7084          |
+| 0.6694        | 0.5257 | 3800 | 0.6951          |
+| 0.7048        | 0.5533 | 4000 | 0.6845          |
+| 0.7265        | 0.5810 | 4200 | 0.6778          |
+| 0.5663        | 0.6087 | 4400 | 0.6657          |
+| 0.6222        | 0.6363 | 4600 | 0.6595          |
+| 0.6463        | 0.6640 | 4800 | 0.6488          |
+| 0.5754        | 0.6917 | 5000 | 0.6410          |
+| 0.6208        | 0.7193 | 5200 | 0.6363          |
+| 0.5613        | 0.7470 | 5400 | 0.6275          |
+| 0.6316        | 0.7747 | 5600 | 0.6227          |
+| 0.6564        | 0.8023 | 5800 | 0.6159          |
+| 0.633         | 0.8300 | 6000 | 0.6077          |
+| 0.5268        | 0.8577 | 6200 | 0.6022          |
+| 0.4166        | 0.8853 | 6400 | 0.5978          |
+| 0.6539        | 0.9130 | 6600 | 0.5926          |
+| 0.5695        | 0.9407 | 6800 | 0.5875          |
+| 0.6358        | 0.9683 | 7000 | 0.5845          |
+| 0.5318        | 0.9960 | 7200 | 0.5829          |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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