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
File size: 380 Bytes
a19bbdd fe7f567 a19bbdd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ---
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. |