Text Classification
Transformers
Safetensors
English
bert
sentiment
english
text-embeddings-inference
Instructions to use ExecuteAutomation/bert-base-text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ExecuteAutomation/bert-base-text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ExecuteAutomation/bert-base-text-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ExecuteAutomation/bert-base-text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("ExecuteAutomation/bert-base-text-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27aad18e2056be5ca18b5c4ded8ef19a7274da9ce6eaeb17367495e2f918b185
- Size of remote file:
- 438 MB
- SHA256:
- 06c5676e4c419ff0e89e3f33d70f0b1d7e5dfab3cffc98dff6af851b27142a8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.