Instructions to use AyoubChLin/bert_wikitext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AyoubChLin/bert_wikitext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="AyoubChLin/bert_wikitext")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AyoubChLin/bert_wikitext") model = AutoModelForMaskedLM.from_pretrained("AyoubChLin/bert_wikitext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bert_wikitext | |
| 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. --> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cherguelainea/huggingface/runs/7urvoez3) | |
| # bert_wikitext | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.5739 | |
| ## 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: 0.0005 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.5777 | 0.3484 | 400 | 2.5606 | | |
| | 3.4402 | 0.6969 | 800 | 3.1688 | | |
| | 3.3722 | 1.0453 | 1200 | 3.2573 | | |
| | 3.1671 | 1.3937 | 1600 | 3.1576 | | |
| | 3.0417 | 1.7422 | 2000 | 3.0741 | | |
| | 2.6817 | 2.0906 | 2400 | 2.8792 | | |
| | 2.5589 | 2.4390 | 2800 | 2.7123 | | |
| | 2.3957 | 2.7875 | 3200 | 2.5590 | | |
| ### Framework versions | |
| - Transformers 4.42.3 | |
| - Pytorch 2.1.2 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |