Instructions to use shed-e/MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shed-e/MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="shed-e/MLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shed-e/MLM") model = AutoModelForMaskedLM.from_pretrained("shed-e/MLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from shed-e/MLM: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/shed-e/MLM/resolve/main/README.md
- Command line
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hf download hf://shed-e/MLM/README.md
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curl -L -o README.md https://huggingface.co/shed-e/MLM/resolve/main/README.md
1.47 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imdb | |
| model-index: | |
| - name: distilbert-base-uncased-finetuned-imdb | |
| 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. --> | |
| # distilbert-base-uncased-finetuned-imdb | |
| 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: 2.4353 | |
| ## 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: 3.0 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 2.6954 | 1.0 | 157 | 2.5243 | | |
| | 2.563 | 2.0 | 314 | 2.4738 | | |
| | 2.5258 | 3.0 | 471 | 2.4369 | | |
| ### Framework versions | |
| - Transformers 4.21.2 | |
| - Pytorch 1.12.1+cu113 | |
| - Datasets 2.4.0 | |
| - Tokenizers 0.12.1 | |