Instructions to use BernardoMSV/modelo_llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BernardoMSV/modelo_llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="BernardoMSV/modelo_llm")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("BernardoMSV/modelo_llm") model = AutoModelForQuestionAnswering.from_pretrained("BernardoMSV/modelo_llm") - Notebooks
- Google Colab
- Kaggle
modelo_llm
This model is a fine-tuned version of pierreguillou/bert-base-cased-squad-v1.1-portuguese on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4656
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 263 | 0.5283 |
| 0.6388 | 2.0 | 526 | 0.5017 |
| 0.6388 | 3.0 | 789 | 0.4508 |
| 0.3642 | 4.0 | 1052 | 0.4479 |
| 0.3642 | 5.0 | 1315 | 0.4656 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.0.1+cpu
- Datasets 2.16.1
- Tokenizers 0.15.1
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