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roberta-base-topic_classification_simple2
Ahmed235/roberta-base-topic_classification_simple2
roberta-base-topic_classification_simple2 is a text classification model from Ahmed235. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jan 25, 2024
- Updated
- Jan 27, 2024
- SHA
- d74c1e1d
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Base models
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jan 25, 2024
- Updated
- Jan 27, 2024