Instructions to use lxs1/DistilBertForSequenceClassification_6h_768dim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lxs1/DistilBertForSequenceClassification_6h_768dim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lxs1/DistilBertForSequenceClassification_6h_768dim")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lxs1/DistilBertForSequenceClassification_6h_768dim") model = AutoModelForSequenceClassification.from_pretrained("lxs1/DistilBertForSequenceClassification_6h_768dim") - Notebooks
- Google Colab
- Kaggle
Fixed model card for correct training hardware to reflect Intel Developer Cloud
Browse files
README.md
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- **Known limitations**: The model may exhibit biases present in the training data, potentially leading to inaccuracies in certain contexts or for specific demographic groups. Its performance has not been extensively tested across all possible domains, so results may vary for texts outside of the training distribution.
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## Hardware
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- **Training Platform**: The model was trained on
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## Software Optimizations
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- **Known Optimizations**: During training, techniques such as gradient accumulation and mixed-precision training were employed to enhance performance and reduce memory usage. The AdamW optimizer was used for its effective learning rate adjustments.
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- **Known limitations**: The model may exhibit biases present in the training data, potentially leading to inaccuracies in certain contexts or for specific demographic groups. Its performance has not been extensively tested across all possible domains, so results may vary for texts outside of the training distribution.
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## Hardware
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- **Training Platform**: The model was trained on Intel Developer Cloud over scalable Intel® Xeon® 4th Gen Scalable processors.
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## Software Optimizations
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- **Known Optimizations**: During training, techniques such as gradient accumulation and mixed-precision training were employed to enhance performance and reduce memory usage. The AdamW optimizer was used for its effective learning rate adjustments.
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