Instructions to use Sybghat/Testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sybghat/Testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Sybghat/Testing")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Sybghat/Testing") model = AutoModelForQuestionAnswering.from_pretrained("Sybghat/Testing") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files
README.md
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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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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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1409
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 1.2386 | 1.0 | 8000 | 1.1409 |
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### Framework versions
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runs/Apr15_09-53-27_ea9db7dea748/events.out.tfevents.1713174810.ea9db7dea748.528.0
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