Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use chjun/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chjun/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chjun/my_awesome_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chjun/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("chjun/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from chjun/my_awesome_model: direct link, hf CLI and curl.
- Browser
- Download file 1.68 kB
-
https://huggingface.co/chjun/my_awesome_model/resolve/main/README.md
- Command line
-
hf download hf://chjun/my_awesome_model/README.md
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curl -L -o README.md https://huggingface.co/chjun/my_awesome_model/resolve/main/README.md
1.68 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imdb | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: my_awesome_model | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: imdb | |
| type: imdb | |
| config: plain_text | |
| split: test | |
| args: plain_text | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.93052 | |
| <!-- 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. --> | |
| # my_awesome_model | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2372 | |
| - Accuracy: 0.9305 | |
| ## 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: 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.2346 | 1.0 | 1563 | 0.1895 | 0.9280 | | |
| | 0.1531 | 2.0 | 3126 | 0.2372 | 0.9305 | | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - Pytorch 1.13.1+cu116 | |
| - Datasets 2.10.1 | |
| - Tokenizers 0.13.2 | |