Instructions to use izaitova/deprel_pl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use izaitova/deprel_pl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="izaitova/deprel_pl")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("izaitova/deprel_pl") model = AutoModelForTokenClassification.from_pretrained("izaitova/deprel_pl") - Notebooks
- Google Colab
- Kaggle
deprel_pl
This model is a fine-tuned version of google/mt5-large on the universal_dependencies 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: 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: 20
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 1.11.0a0+17540c5
- Datasets 2.20.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for izaitova/deprel_pl
Base model
google/mt5-large