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distilbert-base-uncasedv1-finetuned-twitter-sentiment
macildur/distilbert-base-uncasedv1-finetuned-twitter-sentiment
distilbert-base-uncasedv1-finetuned-twitter-sentiment is a text classification model from macildur. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Oct 5, 2022
- Updated
- Oct 5, 2022
- SHA
- d1ff97db
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Datasets
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. -->
distilbert-base-uncasedv1-finetuned-twitter-sentiment
This model is a fine-tuned version of distilbert-base-uncased on the sentiment140 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3985
- Accuracy: 0.8247
- F1: 0.8246
- Precision: 0.8251
- Recall: 0.8017
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 500 | 0.4049 | 0.8181 | 0.8178 | 0.8236 | 0.7862 |
| No log | 2.0 | 1000 | 0.3985 | 0.8247 | 0.8246 | 0.8251 | 0.8017 |
Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Oct 5, 2022
- Updated
- Oct 5, 2022