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-base-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-base-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: 1.8665
+- Accuracy: {'accuracy': 0.7342799188640974}
+- F1: {'f1': 0.7306952447422118}
+
+## 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   | 163  | 1.3840          | {'accuracy': 0.6024340770791075} | {'f1': 0.5642145589948825} |
+| No log        | 2.0   | 326  | 1.0832          | {'accuracy': 0.6511156186612576} | {'f1': 0.6334471187444455} |
+| No log        | 3.0   | 489  | 1.0334          | {'accuracy': 0.6977687626774848} | {'f1': 0.6897630671623124} |
+| 1.0727        | 4.0   | 652  | 1.0970          | {'accuracy': 0.6876267748478702} | {'f1': 0.6871985325785717} |
+| 1.0727        | 5.0   | 815  | 1.0281          | {'accuracy': 0.7342799188640974} | {'f1': 0.7301024691928815} |
+| 1.0727        | 6.0   | 978  | 1.1807          | {'accuracy': 0.7018255578093306} | {'f1': 0.7067299604929954} |
+| 0.2589        | 7.0   | 1141 | 1.2407          | {'accuracy': 0.7342799188640974} | {'f1': 0.7314658348123809} |
+| 0.2589        | 8.0   | 1304 | 1.3048          | {'accuracy': 0.7403651115618661} | {'f1': 0.731151961567854}  |
+| 0.2589        | 9.0   | 1467 | 1.5180          | {'accuracy': 0.718052738336714}  | {'f1': 0.7137872411382804} |
+| 0.0808        | 10.0  | 1630 | 1.3989          | {'accuracy': 0.7606490872210954} | {'f1': 0.7557677624013166} |
+| 0.0808        | 11.0  | 1793 | 1.5029          | {'accuracy': 0.7606490872210954} | {'f1': 0.7552919114782913} |
+| 0.0808        | 12.0  | 1956 | 1.7512          | {'accuracy': 0.7241379310344828} | {'f1': 0.7171770258544846} |
+| 0.0186        | 13.0  | 2119 | 1.6777          | {'accuracy': 0.7363083164300203} | {'f1': 0.7298768119446929} |
+| 0.0186        | 14.0  | 2282 | 1.8128          | {'accuracy': 0.7363083164300203} | {'f1': 0.7328169574773649} |
+| 0.0186        | 15.0  | 2445 | 1.7922          | {'accuracy': 0.7383367139959433} | {'f1': 0.7355194715827496} |
+| 0.0039        | 16.0  | 2608 | 1.8762          | {'accuracy': 0.7281947261663286} | {'f1': 0.7221386387545444} |
+| 0.0039        | 17.0  | 2771 | 1.8840          | {'accuracy': 0.7363083164300203} | {'f1': 0.7317008958800432} |
+| 0.0039        | 18.0  | 2934 | 1.8368          | {'accuracy': 0.7383367139959433} | {'f1': 0.7340167563730315} |
+| 0.0027        | 19.0  | 3097 | 1.8687          | {'accuracy': 0.7363083164300203} | {'f1': 0.7319705371219094} |
+| 0.0027        | 20.0  | 3260 | 1.8665          | {'accuracy': 0.7342799188640974} | {'f1': 0.7306952447422118} |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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