metadata
language: en
license: apache-2.0
tags:
- financial-sentiment-analysis
- sentiment-analysis
- language-perceiver
datasets:
- financial_phrasebank
widget:
- text: INDEX100 fell sharply today.
- text: ImaginaryJetCo bookings hit by Omicron variant as losses total £1bn.
- text: Q1 ImaginaryGame's earnings beat expectations.
- text: Should we buy IMAGINARYSTOCK today?
metrics:
- recall
- f1
- accuracy
- precision
model-index:
- name: fin-perceiver
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: sentences_50agree
metrics:
- name: Accuracy
type: accuracy
value: 0.8624
- name: F1
type: f1
value: 0.8416
args: macro
- name: Precision
type: precision
value: 0.8438
args: macro
- name: Recall
type: recall
value: 0.8415
args: macro
FINPerceiver
FINPerceiver is a fine-tuned Perceiver IO language model for financial sentiment analysis. More details on the training process of this model are available on the GitHub repository.
Weights & Biases was used to track experiments.
We achieved the following results with 10-fold cross validation.
eval/accuracy 0.8624 (stdev 0.01922)
eval/f1 0.8416 (stdev 0.03738)
eval/loss 0.4314 (stdev 0.05295)
eval/precision 0.8438 (stdev 0.02938)
eval/recall 0.8415 (stdev 0.04458)
The hyperparameters used are as follows.
per_device_train_batch_size 16
per_device_eval_batch_size 16
num_train_epochs 4
learning_rate 2e-5
Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)