Instructions to use thepian/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thepian/checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="thepian/checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("thepian/checkpoints") model = AutoModelForTokenClassification.from_pretrained("thepian/checkpoints") - Notebooks
- Google Colab
- Kaggle
Fine-tuned on eco-products domain (brand, product name, origin)
Browse files- README.md +2 -2
- best/model.safetensors +1 -1
- best/training_args.bin +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [bltlab/queryner-bert-base-uncased](https://huggingface.co/bltlab/queryner-bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| 1.9302 | 1.0 | 797 | 0.1401 |
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### Framework versions
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This model is a fine-tuned version of [bltlab/queryner-bert-base-uncased](https://huggingface.co/bltlab/queryner-bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1055
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## Model description
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| 1.9302 | 1.0 | 797 | 0.1401 |
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| 0.163 | 2.0 | 1594 | 0.1058 |
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| 0.1035 | 3.0 | 2391 | 0.1055 |
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### Framework versions
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best/model.safetensors
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