Instructions to use EndLessTime/fine_tuned_per_domain_balanced_32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EndLessTime/fine_tuned_per_domain_balanced_32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EndLessTime/fine_tuned_per_domain_balanced_32B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EndLessTime/fine_tuned_per_domain_balanced_32B") model = AutoModelForSequenceClassification.from_pretrained("EndLessTime/fine_tuned_per_domain_balanced_32B") - Notebooks
- Google Colab
- Kaggle
Arihant Tripathi commited on
qwen_new_mage_per_domain_balanced_32B
Browse files- README.md +15 -15
- evaluation_results.json +6 -6
- model-00001-of-00014.safetensors +1 -1
- model-00002-of-00014.safetensors +1 -1
- model-00003-of-00014.safetensors +1 -1
- model-00004-of-00014.safetensors +1 -1
- model-00005-of-00014.safetensors +1 -1
- model-00006-of-00014.safetensors +1 -1
- model-00007-of-00014.safetensors +1 -1
- model-00008-of-00014.safetensors +1 -1
- model-00009-of-00014.safetensors +1 -1
- model-00010-of-00014.safetensors +1 -1
- model-00011-of-00014.safetensors +1 -1
- model-00012-of-00014.safetensors +1 -1
- model-00013-of-00014.safetensors +1 -1
- model-00014-of-00014.safetensors +1 -1
README.md
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This model is a fine-tuned version of [Qwen/Qwen1.5-32B](https://huggingface.co/Qwen/Qwen1.5-32B) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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### Framework versions
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This model is a fine-tuned version of [Qwen/Qwen1.5-32B](https://huggingface.co/Qwen/Qwen1.5-32B) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5444
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- Accuracy: 0.9033
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:--------:|
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| 0.8741 | 0.0029 | 500 | 0.9781 | 0.7780 |
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| 0.8585 | 0.0057 | 1000 | 0.8674 | 0.8389 |
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| 1.0133 | 0.0086 | 1500 | 0.9428 | 0.8263 |
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| 1.0197 | 0.0114 | 2000 | 0.7949 | 0.8532 |
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| 0.4964 | 0.0143 | 2500 | 0.8058 | 0.8585 |
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| 0.507 | 0.0172 | 3000 | 0.6268 | 0.8756 |
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| 1.2342 | 0.0200 | 3500 | 0.6786 | 0.8711 |
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| 0.8958 | 0.0229 | 4000 | 0.6737 | 0.8782 |
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| 0.5373 | 0.0258 | 4500 | 0.5536 | 0.8926 |
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| 0.3688 | 0.0286 | 5000 | 0.5243 | 0.8988 |
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| 0.2458 | 0.0315 | 5500 | 0.5292 | 0.8997 |
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| 0.5241 | 0.0343 | 6000 | 0.5510 | 0.9024 |
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| 0.1869 | 0.0372 | 6500 | 0.5444 | 0.9033 |
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### Framework versions
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evaluation_results.json
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"eval_loss": 0.
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"eval_accuracy": 0.
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"eval_runtime":
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"eval_samples_per_second": 3.
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"eval_steps_per_second": 3.
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"epoch": 0.
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{
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"eval_loss": 0.7579994797706604,
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"eval_accuracy": 0.8836168307967771,
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"eval_runtime": 355.6167,
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"eval_samples_per_second": 3.141,
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"eval_steps_per_second": 3.141,
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"epoch": 0.037194930044920034
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}
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