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sentiment_ita
luigisaetta/sentiment_ita
sentiment_ita is a text classification model from luigisaetta. 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
- bert
- Access
- Public
- Created
- Nov 23, 2023
- Updated
- Nov 26, 2023
- SHA
- 5aaa7176
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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. -->
sentiment_ita
This model is a fine-tuned version of neuraly/bert-base-italian-cased-sentiment on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6301
- Accuracy: 0.6903
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: 48
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 14
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5002 | 1.67 | 100 | 0.9894 | 0.6460 |
| 0.6959 | 3.33 | 200 | 0.7681 | 0.6726 |
| 0.5325 | 5.0 | 300 | 0.7615 | 0.6962 |
| 0.349 | 6.67 | 400 | 0.8867 | 0.6932 |
| 0.1798 | 8.33 | 500 | 1.1361 | 0.6873 |
| 0.0983 | 10.0 | 600 | 1.3994 | 0.6962 |
| 0.0412 | 11.67 | 700 | 1.5411 | 0.7109 |
| 0.0293 | 13.33 | 800 | 1.6301 | 0.6903 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Nov 23, 2023
- Updated
- Nov 26, 2023