Instructions to use ViditRaj/BERT_LATEST_Ads_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViditRaj/BERT_LATEST_Ads_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViditRaj/BERT_LATEST_Ads_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViditRaj/BERT_LATEST_Ads_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ViditRaj/BERT_LATEST_Ads_Classifier") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 4
Browse files- README.md +6 -4
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.
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- Train Accuracy: 0.
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- Epoch:
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## Model description
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| 0.3301 | 0.2277 | 0.9150 | 0 |
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| 0.1607 | 0.1973 | 0.9235 | 1 |
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| 0.0958 | 0.1931 | 0.9348 | 2 |
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0433
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- Validation Loss: 0.2119
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- Train Accuracy: 0.9320
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- Epoch: 4
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## Model description
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| 0.3301 | 0.2277 | 0.9150 | 0 |
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| 0.1607 | 0.1973 | 0.9235 | 1 |
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| 0.0958 | 0.1931 | 0.9348 | 2 |
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| 0.0592 | 0.2309 | 0.9292 | 3 |
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| 0.0433 | 0.2119 | 0.9320 | 4 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 438223192
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