Instructions to use farid1088/qa_bert_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use farid1088/qa_bert_training 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="farid1088/qa_bert_training")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("farid1088/qa_bert_training") model = AutoModelForQuestionAnswering.from_pretrained("farid1088/qa_bert_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from farid1088/qa_bert_training: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/farid1088/qa_bert_training/resolve/main/README.md
- Command line
-
hf download hf://farid1088/qa_bert_training/README.md
-
curl -L -o README.md https://huggingface.co/farid1088/qa_bert_training/resolve/main/README.md
1.1 kB
metadata
tags:
- generated_from_trainer
model-index:
- name: qa_bert_training
results: []
qa_bert_training
This model was trained from scratch 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: 16
- 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 | 20 | 4.1789 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.7
- Tokenizers 0.15.0