Instructions to use engindemir/bart_tr_dependencyparsing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use engindemir/bart_tr_dependencyparsing with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("engindemir/bart_tr_dependencyparsing") model = AutoModelForSeq2SeqLM.from_pretrained("engindemir/bart_tr_dependencyparsing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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base_model: facebook/bart-base
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library_name: transformers
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license: apache-2.0
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tags:
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- generated_from_trainer
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model-index:
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/bart-base
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tags:
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- generated_from_trainer
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model-index:
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0321
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.3947 | 1.1641 | 1000 | 0.0453 |
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| 0.0498 | 2.3283 | 2000 | 0.0321 |
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### Framework versions
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