Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use Zarkit/classificationEsp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zarkit/classificationEsp2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zarkit/classificationEsp2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zarkit/classificationEsp2") model = AutoModelForSequenceClassification.from_pretrained("Zarkit/classificationEsp2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Zarkit/classificationEsp2: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Zarkit/classificationEsp2/resolve/main/tf_model.h5
- Command line
-
hf download hf://Zarkit/classificationEsp2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Zarkit/classificationEsp2/resolve/main/tf_model.h5
499 MB
- Xet hash:
- 5d4aac3ae9b10dd33f507f630ab4e57475a2d4397e6401a5f9dfc0530f53b4e4
- Size of remote file:
- 499 MB
- SHA256:
- ac3c8a46f0d679bab350bec395927f7199ed66cbee6b53c8bbb7e9149836cab5
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