Instructions to use Jose-Ribeir/second_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jose-Ribeir/second_try 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="Jose-Ribeir/second_try")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Jose-Ribeir/second_try") model = AutoModelForQuestionAnswering.from_pretrained("Jose-Ribeir/second_try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Jose-Ribeir/second_try: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/Jose-Ribeir/second_try/resolve/main/README.md
- Command line
-
hf download hf://Jose-Ribeir/second_try/README.md
-
curl -L -o README.md https://huggingface.co/Jose-Ribeir/second_try/resolve/main/README.md
1.16 kB
metadata
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: second_try
results: []
second_try
This model is a fine-tuned version of deepset/roberta-base-squad2 on the None 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1