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
| 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. |