Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use matmei/MulticlassReaction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matmei/MulticlassReaction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matmei/MulticlassReaction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("matmei/MulticlassReaction") model = AutoModelForSequenceClassification.from_pretrained("matmei/MulticlassReaction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from matmei/MulticlassReaction: direct link, hf CLI and curl.
- Browser
- Download file 1.61 kB
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https://huggingface.co/matmei/MulticlassReaction/resolve/main/README.md
- Command line
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hf download hf://matmei/MulticlassReaction/README.md
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curl -L -o README.md https://huggingface.co/matmei/MulticlassReaction/resolve/main/README.md
1.61 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: MulticlassReaction | |
| results: [] | |
| <!-- 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. --> | |
| # MulticlassReaction | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5756 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 15 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.6916 | 1.0 | 345 | 0.6023 | | |
| | 0.4854 | 2.0 | 690 | 0.5756 | | |
| | 0.3072 | 3.0 | 1035 | 0.6279 | | |
| | 0.1727 | 4.0 | 1380 | 0.8522 | | |
| ### Framework versions | |
| - Transformers 5.16.1 | |
| - Pytorch 2.14.0+cpu | |
| - Datasets 5.0.1 | |
| - Tokenizers 0.23.2 | |