Instructions to use aman38649/distilbert-base-uncased-finetuned-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aman38649/distilbert-base-uncased-finetuned-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aman38649/distilbert-base-uncased-finetuned-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aman38649/distilbert-base-uncased-finetuned-imdb") model = AutoModelForMaskedLM.from_pretrained("aman38649/distilbert-base-uncased-finetuned-imdb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from aman38649/distilbert-base-uncased-finetuned-imdb: direct link, hf CLI and curl.
- Browser
- Download file 1.75 kB
-
https://huggingface.co/aman38649/distilbert-base-uncased-finetuned-imdb/resolve/main/README.md
- Command line
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hf download hf://aman38649/distilbert-base-uncased-finetuned-imdb/README.md
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curl -L -o README.md https://huggingface.co/aman38649/distilbert-base-uncased-finetuned-imdb/resolve/main/README.md
1.75 kB
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: aman38649/distilbert-base-uncased-finetuned-imdb | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # aman38649/distilbert-base-uncased-finetuned-imdb | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 2.8608 | |
| - Validation Loss: 2.5995 | |
| - Epoch: 0 | |
| ## 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: | |
| - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} | |
| - training_precision: mixed_float16 | |
| ### Training results | |
| | Train Loss | Validation Loss | Epoch | | |
| |:----------:|:---------------:|:-----:| | |
| | 2.8608 | 2.5995 | 0 | | |
| ### Framework versions | |
| - Transformers 4.31.0 | |
| - TensorFlow 2.12.0 | |
| - Datasets 2.14.0 | |
| - Tokenizers 0.13.3 | |