README.md

---
language: 
- en
tags:
- text-classification
- emotion
- pytorch
license: mit
datasets:
- emotion
metrics:
- accuracy
- precision
- recall
- f1
---

# bert-base-uncased-emotion

## Model description

`bert-base-uncased` finetuned on the unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets) [~250K texts with 7 labels -- neutral, happy, sad, anger, disgust, surprise, fear], then transferred to
a small sample of 10K hand-tagged StockTwits messages. Optimized for extracting emotions from financial social media, such as StockTwits.  

Sequence length 64, learning rate 2e-5, batch size 128, 8 epochs.

For inference, follow the Inference.ipynb notebook.

## Training data

Data came from https://github.com/sarnthil/unify-emotion-datasets.