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-topic_classification_simple2
+  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-topic_classification_simple2
+
+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: 1.1250
+- Accuracy: {'accuracy': 0.866996699669967}
+- F1: {'f1': 0.8657113367537151}
+
+## 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   | 313  | 0.5920          | {'accuracy': 0.8158415841584158} | {'f1': 0.8063426391052376} |
+| 0.7507        | 2.0   | 626  | 0.5183          | {'accuracy': 0.8419141914191419} | {'f1': 0.8450438669495921} |
+| 0.7507        | 3.0   | 939  | 0.5089          | {'accuracy': 0.8514851485148515} | {'f1': 0.8522994355907825} |
+| 0.3199        | 4.0   | 1252 | 0.6030          | {'accuracy': 0.8508250825082508} | {'f1': 0.8484331857141633} |
+| 0.1504        | 5.0   | 1565 | 0.6894          | {'accuracy': 0.8617161716171617} | {'f1': 0.8599694556754336} |
+| 0.1504        | 6.0   | 1878 | 0.8381          | {'accuracy': 0.8448844884488449} | {'f1': 0.8461993387843019} |
+| 0.0822        | 7.0   | 2191 | 0.8515          | {'accuracy': 0.8554455445544554} | {'f1': 0.8542784950089077} |
+| 0.0551        | 8.0   | 2504 | 0.9319          | {'accuracy': 0.8531353135313532} | {'f1': 0.853451943641699}  |
+| 0.0551        | 9.0   | 2817 | 0.9478          | {'accuracy': 0.8577557755775578} | {'f1': 0.8565849659994866} |
+| 0.0377        | 10.0  | 3130 | 0.9998          | {'accuracy': 0.8554455445544554} | {'f1': 0.8550659197552203} |
+| 0.0377        | 11.0  | 3443 | 1.0025          | {'accuracy': 0.8554455445544554} | {'f1': 0.8550137537621838} |
+| 0.0279        | 12.0  | 3756 | 1.0728          | {'accuracy': 0.8574257425742574} | {'f1': 0.8566278925949554} |
+| 0.0132        | 13.0  | 4069 | 1.0873          | {'accuracy': 0.8623762376237624} | {'f1': 0.8610125122049608} |
+| 0.0132        | 14.0  | 4382 | 1.0989          | {'accuracy': 0.8653465346534653} | {'f1': 0.863969705278768}  |
+| 0.0124        | 15.0  | 4695 | 1.1379          | {'accuracy': 0.8643564356435643} | {'f1': 0.8630599594036119} |
+| 0.0095        | 16.0  | 5008 | 1.1207          | {'accuracy': 0.8653465346534653} | {'f1': 0.8639194427774014} |
+| 0.0095        | 17.0  | 5321 | 1.1053          | {'accuracy': 0.866006600660066}  | {'f1': 0.8652013668499585} |
+| 0.0074        | 18.0  | 5634 | 1.1296          | {'accuracy': 0.863036303630363}  | {'f1': 0.8615189712315606} |
+| 0.0074        | 19.0  | 5947 | 1.1099          | {'accuracy': 0.8689768976897689} | {'f1': 0.867663744149239}  |
+| 0.0046        | 20.0  | 6260 | 1.1250          | {'accuracy': 0.866996699669967}  | {'f1': 0.8657113367537151} |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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