Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use amirapppppp79/my_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amirapppppp79/my_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amirapppppp79/my_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amirapppppp79/my_model") model = AutoModelForSequenceClassification.from_pretrained("amirapppppp79/my_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d6ac25775f71227f642397cd6637384e3dcf598bd45d329ea01026df74982fb
- Size of remote file:
- 473 MB
- SHA256:
- d210357e1a35a4b9e2da1cac7729dc5d1b1123c93ad9b68c02773a6bf17d6bd3
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