Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
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README.md
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metrics:
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model-index:
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- name: distilbert-base-uncased-nvidia-aegis-v2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-base-uncased-nvidia-aegis-v2
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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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## Model description
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### Training results
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### Framework versions
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metrics:
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- accuracy
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model-index:
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- name: distilbert-base-uncased-nvidia-aegis-v2-augmented
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# distilbert-base-uncased-nvidia-aegis-v2-augmented
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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: 0.2818
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- Accuracy: 0.8756
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| 0.8393 | 1.0 | 8402 | 0.3430 | 0.8395 |
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| 0.4610 | 2.0 | 16804 | 0.2815 | 0.8758 |
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| 0.2319 | 3.0 | 25206 | 0.3292 | 0.8862 |
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| 0.3144 | 4.0 | 33608 | 0.3165 | 0.8944 |
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| 0.1178 | 5.0 | 42010 | 0.3211 | 0.9026 |
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### Framework versions
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model.safetensors
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