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amazon-reviews-sentiment-bert-base-uncased-6000-samples
NannyML/amazon-reviews-sentiment-bert-base-uncased-6000-samples
amazon-reviews-sentiment-bert-base-uncased-6000-samples is a text classification model from NannyML. 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
- Sep 28, 2023
- Updated
- Oct 6, 2023
- SHA
- f31002cf
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Base models
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. -->
amazon-reviews-sentiment-bert-base-uncased-6000-samples
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set:
- Loss: 0.5890
- Accuracy: 0.7679
- F1: 0.7168
Predicted labels
- LABEL_0: Negative review
- LABEL_1: Neutral review
- LABEL_2: Positive review
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 188 | 0.5745 | 0.7586 | 0.7149 |
| No log | 2.0 | 376 | 0.5890 | 0.7679 | 0.7168 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.0
- Datasets 2.14.6.dev0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Sep 28, 2023
- Updated
- Oct 6, 2023