Instructions to use mishbahulal/distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mishbahulal/distilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mishbahulal/distilbert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mishbahulal/distilbert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("mishbahulal/distilbert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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A **BERT-based emotion classification model** fine-tuned on the **GoEmotions dataset** to detect emotions in English text.
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The model predicts emotional categories from input sentences and can be used for **sentiment analysis, emotion detection, and conversational AI systems**.
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# Model Details
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A **BERT-based emotion classification model** fine-tuned on the **GoEmotions dataset** to detect emotions in English text.
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The model predicts emotional categories from input sentences and can be used for **sentiment analysis, emotion detection, and conversational AI systems**.
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version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English.
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**DistilBERT base model (uncased)**
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This model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English.
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# Model Details
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