End of training

README.md

@@ -1,3 +1,82 @@
----
-license: mit
----
+---
+library_name: transformers
+license: mit
+base_model: roberta-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: cricket-project-01
+  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. -->
+
+# cricket-project-01
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2588
+- Accuracy: 0.9361
+- Precision: 0.4680
+- Recall: 0.5
+- F1: 0.4835
+
+## 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: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 7
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.3406        | 0.4318 | 500  | 0.2407          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3133        | 0.8636 | 1000 | 0.2432          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3181        | 1.2953 | 1500 | 0.2443          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3135        | 1.7271 | 2000 | 0.2726          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3228        | 2.1589 | 2500 | 0.2730          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3226        | 2.5907 | 3000 | 0.2750          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3171        | 3.0225 | 3500 | 0.2741          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3171        | 3.4542 | 4000 | 0.2625          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3056        | 3.8860 | 4500 | 0.2791          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3468        | 4.3178 | 5000 | 0.2645          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3099        | 4.7496 | 5500 | 0.2540          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.2992        | 5.1813 | 6000 | 0.2543          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3321        | 5.6131 | 6500 | 0.2719          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.32          | 6.0449 | 7000 | 0.2699          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3153        | 6.4767 | 7500 | 0.2643          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+| 0.3278        | 6.9085 | 8000 | 0.2588          | 0.9361   | 0.4680    | 0.5    | 0.4835 |
+
+
+### Framework versions
+
+- Transformers 4.50.3
+- Pytorch 2.6.0+cu124
+- Tokenizers 0.21.1