Instructions to use nirupamadasari/bartlfqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirupamadasari/bartlfqa with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="nirupamadasari/bartlfqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("nirupamadasari/bartlfqa") model = AutoModelForQuestionAnswering.from_pretrained("nirupamadasari/bartlfqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from nirupamadasari/bartlfqa: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/nirupamadasari/bartlfqa/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://nirupamadasari/bartlfqa@refs/pr/1/README.md
-
curl -L -o README.md https://huggingface.co/nirupamadasari/bartlfqa/resolve/refs%2Fpr%2F1/README.md
1.16 kB
| license: mit | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bartlfqa | |
| 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. --> | |
| # bartlfqa | |
| This model is a fine-tuned version of [vblagoje/bart_lfqa](https://huggingface.co/vblagoje/bart_lfqa) on an unknown dataset. | |
| ## 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: 0.0001 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 11 | 3.8251 | | |
| ### Framework versions | |
| - Transformers 4.30.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.13.1 | |
| - Tokenizers 0.13.3 | |