Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use leomaurodesenv/roberta-base-answerable-or-not-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/roberta-base-answerable-or-not-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/roberta-base-answerable-or-not-augmented")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/roberta-base-answerable-or-not-augmented") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/roberta-base-answerable-or-not-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files- README.md +13 -14
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README.md
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metrics:
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- accuracy
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model-index:
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- name: roberta-base-answerable-or-not
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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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# roberta-base-answerable-or-not
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) 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: roberta-base-answerable-or-not
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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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# roberta-base-answerable-or-not
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0507
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- Accuracy: 0.9949
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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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| 0.1538 | 1.0 | 198 | 0.1729 | 0.9696 |
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| 0.1172 | 2.0 | 396 | 0.1149 | 0.9823 |
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| 0.0325 | 3.0 | 594 | 0.0816 | 0.9873 |
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| 0.0001 | 4.0 | 792 | 0.0507 | 0.9949 |
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| 0.0001 | 5.0 | 990 | 0.0844 | 0.9899 |
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| 0.0000 | 6.0 | 1188 | 0.0528 | 0.9949 |
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| 0.0000 | 7.0 | 1386 | 0.0559 | 0.9949 |
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### Framework versions
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