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
-
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
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of 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