End of training

README.md

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+---
+license: mit
+base_model: roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: roberta_classification
+  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. -->
+
+# roberta_classification
+
+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: 1.2731
+- Accuracy: {'accuracy': 0.8465909090909091}
+- F1: {'f1': 0.8396445042099528}
+
+## 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: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
+| No log        | 1.0   | 263  | 1.1741          | {'accuracy': 0.6363636363636364} | {'f1': 0.6202787331893512} |
+| 1.181         | 2.0   | 526  | 0.9322          | {'accuracy': 0.7386363636363636} | {'f1': 0.7177199655598837} |
+| 1.181         | 3.0   | 789  | 0.7835          | {'accuracy': 0.7727272727272727} | {'f1': 0.7657783584890875} |
+| 0.3689        | 4.0   | 1052 | 0.8597          | {'accuracy': 0.7727272727272727} | {'f1': 0.768360357103512}  |
+| 0.3689        | 5.0   | 1315 | 0.7560          | {'accuracy': 0.8125}             | {'f1': 0.8031513875852524} |
+| 0.165         | 6.0   | 1578 | 0.7579          | {'accuracy': 0.8200757575757576} | {'f1': 0.8142845258630059} |
+| 0.165         | 7.0   | 1841 | 0.8900          | {'accuracy': 0.8352272727272727} | {'f1': 0.8316422201059607} |
+| 0.0778        | 8.0   | 2104 | 0.9315          | {'accuracy': 0.8295454545454546} | {'f1': 0.825285136658407}  |
+| 0.0778        | 9.0   | 2367 | 1.1370          | {'accuracy': 0.8181818181818182} | {'f1': 0.8091288762824846} |
+| 0.0335        | 10.0  | 2630 | 1.0799          | {'accuracy': 0.8465909090909091} | {'f1': 0.841700330957688}  |
+| 0.0335        | 11.0  | 2893 | 1.2487          | {'accuracy': 0.8314393939393939} | {'f1': 0.8269815181159639} |
+| 0.0162        | 12.0  | 3156 | 1.2194          | {'accuracy': 0.8295454545454546} | {'f1': 0.8243565671691487} |
+| 0.0162        | 13.0  | 3419 | 1.2592          | {'accuracy': 0.8333333333333334} | {'f1': 0.8312612314115424} |
+| 0.0073        | 14.0  | 3682 | 1.2885          | {'accuracy': 0.8257575757575758} | {'f1': 0.8198413592956925} |
+| 0.0073        | 15.0  | 3945 | 1.2133          | {'accuracy': 0.8352272727272727} | {'f1': 0.8291568008253063} |
+| 0.0046        | 16.0  | 4208 | 1.2625          | {'accuracy': 0.8409090909090909} | {'f1': 0.8343252944129244} |
+| 0.0046        | 17.0  | 4471 | 1.2498          | {'accuracy': 0.8409090909090909} | {'f1': 0.8356461395476784} |
+| 0.0032        | 18.0  | 4734 | 1.3041          | {'accuracy': 0.8390151515151515} | {'f1': 0.8307896138032654} |
+| 0.0032        | 19.0  | 4997 | 1.2544          | {'accuracy': 0.8446969696969697} | {'f1': 0.83889081905153}   |
+| 0.0022        | 20.0  | 5260 | 1.2731          | {'accuracy': 0.8465909090909091} | {'f1': 0.8396445042099528} |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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