---
license: apache-2.0
base_model: Twitter/twhin-bert-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: financial-twhin-bert-large-3labels
  results: []
datasets:
- zeroshot/twitter-financial-news-sentiment
language:
- en
widget:
- text: "$KTOS: Kratos Defense and Security awarded a $39 million sole-source contract for Geolocation Global Support Service"
  example_title: "Example 1"
- text: "$Google parent Alphabet Inc. reported revenue and earnings that fell short of analysts' expectations, showing the company's search advertising juggernaut was not immune to a slowdown in the digital ad market. The shares fell more than 6%."
  example_title: "Example 2"
- text: "$LJPC - La Jolla Pharma to reassess development of LJPC-401"
  example_title: "Example 3"
- text: "Watch $MARK over 43c in after-hours for continuation targeting the 50c area initially"
  example title: "Example 4"
- text: "$RCII: Rent-A-Center provides update - March revenues were off by about 5% versus last year"
  example title: "Example 5"
---

# financial-twhin-bert-large-3labels

This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on finance related tweets.
It achieves the following results on the evaluation set:
- Loss: 0.2959
- Accuracy: 0.8934
- F1: 0.8943

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2.0998212817984933e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2