End of training

README.md

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+---
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: EVALutionRelationTrain-4
+  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. -->
+
+# EVALutionRelationTrain-4
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6932
+- Accuracy: 0.5
+- Precision: 0.5
+- Recall: 1.0
+- F1: 0.6667
+
+## 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: 5e-05
+- 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: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| No log        | 0.35  | 100  | 0.7150          | 0.5      | 0.0       | 0.0    | 0.0    |
+| No log        | 0.71  | 200  | 0.7055          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| No log        | 1.06  | 300  | 0.6936          | 0.5      | 0.0       | 0.0    | 0.0    |
+| No log        | 1.42  | 400  | 0.6939          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7014        | 1.77  | 500  | 0.6984          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7014        | 2.13  | 600  | 0.7032          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7014        | 2.48  | 700  | 0.6952          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| 0.7014        | 2.84  | 800  | 0.6989          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7014        | 3.19  | 900  | 0.6935          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| 0.7002        | 3.55  | 1000 | 0.6949          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7002        | 3.9   | 1100 | 0.6951          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7002        | 4.26  | 1200 | 0.6935          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.7002        | 4.61  | 1300 | 0.6985          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| 0.7002        | 4.96  | 1400 | 0.6945          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.6969        | 5.32  | 1500 | 0.6932          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| 0.6969        | 5.67  | 1600 | 0.6964          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.6969        | 6.03  | 1700 | 0.7006          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.6969        | 6.38  | 1800 | 0.6934          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.6969        | 6.74  | 1900 | 0.6966          | 0.5      | 0.0       | 0.0    | 0.0    |
+| 0.6939        | 7.09  | 2000 | 0.6947          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| 0.6939        | 7.45  | 2100 | 0.6935          | 0.5      | 0.5       | 1.0    | 0.6667 |
+| 0.6939        | 7.8   | 2200 | 0.6932          | 0.5      | 0.5       | 1.0    | 0.6667 |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.1.0+cu121
+- Datasets 2.14.5
+- Tokenizers 0.14.1

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