Instructions to use evelinamorim/token_classification_agreement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evelinamorim/token_classification_agreement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="evelinamorim/token_classification_agreement")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("evelinamorim/token_classification_agreement") model = AutoModelForTokenClassification.from_pretrained("evelinamorim/token_classification_agreement", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from evelinamorim/token_classification_agreement: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
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https://huggingface.co/evelinamorim/token_classification_agreement/resolve/main/README.md
- Command line
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hf download hf://evelinamorim/token_classification_agreement/README.md
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curl -L -o README.md https://huggingface.co/evelinamorim/token_classification_agreement/resolve/main/README.md
1.21 kB
metadata
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
model-index:
- name: token_classification_agreement
results: []
token_classification_agreement
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1556
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: 3
Training results
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1