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")# pip install -U transformers accelerate # 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
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Download README.md from ExecuteAutomation/bert-base-text-classification-model: direct link, hf CLI and curl.
- Browser
- Download file 380 Bytes
-
https://huggingface.co/ExecuteAutomation/bert-base-text-classification-model/resolve/main/README.md
- Command line
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hf download hf://ExecuteAutomation/bert-base-text-classification-model/README.md
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curl -L -o README.md https://huggingface.co/ExecuteAutomation/bert-base-text-classification-model/resolve/main/README.md
380 Bytes
metadata
license: mit
datasets:
- dair-ai/emotion
language:
- en
metrics:
- accuracy
- f1
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
tags:
- sentiment
- english
library_name: transformers
Bert-base-text-classification-model
This model is trained using Bert-base-uncased model as the based model which is helpful for Multi Text classification.