Instructions to use apssg96/distilbert-base-uncased-finetuned-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apssg96/distilbert-base-uncased-finetuned-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="apssg96/distilbert-base-uncased-finetuned-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("apssg96/distilbert-base-uncased-finetuned-emotion") model = AutoModelForSequenceClassification.from_pretrained("apssg96/distilbert-base-uncased-finetuned-emotion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ca8af44d01ec8c659aa212bc9a9d2544879f374780686d58f65dde01ab3f43c
- Size of remote file:
- 268 MB
- SHA256:
- 07f15a4720e58581acb100b637bf02369535945af4cbb2c5cdc1caef93f0d185
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