Instructions to use anandshende/my_awesome_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anandshende/my_awesome_qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="anandshende/my_awesome_qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("anandshende/my_awesome_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("anandshende/my_awesome_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit 路
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Parent(s): 90e63b5
update model card README.md
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README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad 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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| No log | 1.0 | 250 | 2.
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### Framework versions
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- Transformers 4.
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- Pytorch 2.0.1+
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6589
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 250 | 2.3366 |
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| 2.8308 | 2.0 | 500 | 1.7977 |
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| 2.8308 | 3.0 | 750 | 1.6589 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1+cpu
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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