Instructions to use krinal214/zero_shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krinal214/zero_shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="krinal214/zero_shot")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("krinal214/zero_shot") model = AutoModelForQuestionAnswering.from_pretrained("krinal214/zero_shot") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: zero_last
results: []
zero_last
This model is a fine-tuned version of bert-base-multilingual-cased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.9190
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: 16
- 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 |
|---|---|---|---|
| 0.9816 | 1.0 | 5557 | 1.9190 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.9.1
- Datasets 2.0.0
- Tokenizers 0.10.3