Instructions to use Mikimi/twitter_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mikimi/twitter_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mikimi/twitter_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mikimi/twitter_trainer") model = AutoModelForSequenceClassification.from_pretrained("Mikimi/twitter_trainer") - Notebooks
- Google Colab
- Kaggle
bert-base-case-financial-news-twitter-sentiment
Browse files- README.md +14 -14
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- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy:
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- P:
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## Model description
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### Training results
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| Training Loss | Epoch
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### Framework versions
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7924
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- Accuracy: 86.8509
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- P: 102.7555
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- R: 100.3442
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- F1: 101.5355
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | P | R | F1 |
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| 3.6245 | 1.0 | 597 | 0.4451 | 84.1709 | 99.8149 | 103.1569 | 101.4584 |
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| 1.8323 | 2.0 | 1194 | 0.3794 | 86.0972 | 102.3665 | 100.0625 | 101.2014 |
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| 1.233 | 3.0 | 1791 | 0.3715 | 87.5209 | 100.9234 | 102.3408 | 101.6272 |
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| 0.9132 | 4.0 | 2388 | 0.5171 | 87.1022 | 102.4483 | 100.4991 | 101.4643 |
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| 0.6928 | 5.0 | 2985 | 0.6683 | 86.9347 | 102.6526 | 100.5006 | 101.5652 |
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| 0.4037 | 6.0 | 3582 | 0.7477 | 87.3534 | 101.8838 | 101.3746 | 101.6286 |
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| 0.3334 | 6.9891 | 4172 | 0.7924 | 86.8509 | 102.7555 | 100.3442 | 101.5355 |
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### Framework versions
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model.safetensors
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training_args.bin
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