Instructions to use plncmm/bert-clinical-scratch-wl-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plncmm/bert-clinical-scratch-wl-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="plncmm/bert-clinical-scratch-wl-es")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("plncmm/bert-clinical-scratch-wl-es") model = AutoModelForMaskedLM.from_pretrained("plncmm/bert-clinical-scratch-wl-es") - Notebooks
- Google Colab
- Kaggle
bert-clincal-scratch-wl-es
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-uncased on an unknown 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
- Downloads last month
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Model tree for plncmm/bert-clinical-scratch-wl-es
Base model
dccuchile/bert-base-spanish-wwm-uncased