README.md

metadata

license: apache-2.0
base_model: Twitter/twhin-bert-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: financial_twhin_bert_large_7labels
    results: []
datasets:
  - FinGPT/fingpt-sentiment-train
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-7labels

This model is a fine-tuned version of Twitter/twhin-bert-large on 61K financial tweets. It achieves the following results on the evaluation set:

• Loss: 0.7361
• Accuracy: 0.8946
• F1: 0.8969

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: 6.284712783126724e-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1