Instructions to use 21iridescent/Relation-Extractor-ComSci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 21iridescent/Relation-Extractor-ComSci with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("21iridescent/Relation-Extractor-ComSci") model = AutoModelForSeq2SeqLM.from_pretrained("21iridescent/Relation-Extractor-ComSci", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from 21iridescent/Relation-Extractor-ComSci: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
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https://huggingface.co/21iridescent/Relation-Extractor-ComSci/resolve/main/README.md
- Command line
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hf download hf://21iridescent/Relation-Extractor-ComSci/README.md
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curl -L -o README.md https://huggingface.co/21iridescent/Relation-Extractor-ComSci/resolve/main/README.md
1.34 kB
metadata
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: output
results: []
output
This model is a fine-tuned version of Babelscape/rebel-large 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 | Precision | Recall | F1-measure |
|---|---|---|---|---|---|---|
| No log | 1.0 | 236 | 0.3225 | 0.8889 | 0.8889 | 0.8889 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.9.0+cu111
- Datasets 2.4.0
- Tokenizers 0.12.1