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Auditor Review Sentiment Model

This model has been finetuned from the proprietary version of FinBERT trained internally using demo.org proprietary dataset of auditor evaluation of sentiment.

FinBERT is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in the financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model.

Training Data

This model was fine-tuned using Autotrain from the demo-org/auditor_review review dataset.

Model Status

This model is currently being evaluated in development until the end of the quarter. Based on the results, it may be elevated to production.

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0002
  • 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
  • num_epochs: 4
  • mixed_precision_training: Native AMP

Model Trained Using AutoTrain

  • Problem type: Multi-class Classification
  • Model ID: 1167143226
  • CO2 Emissions (in grams): 3.165771608457648

Validation Metrics

  • Loss: 0.3418470025062561
  • Accuracy: 0.8617131062951496
  • Macro F1: 0.8448284352912685
  • Micro F1: 0.8617131062951496
  • Weighted F1: 0.8612696670395574
  • Macro Precision: 0.8440532616584138
  • Micro Precision: 0.8617131062951496
  • Weighted Precision: 0.8612762332366959
  • Macro Recall: 0.8461980005490884
  • Micro Recall: 0.8617131062951496
  • Weighted Recall: 0.8617131062951496

Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/rajistics/autotrain-auditor-sentiment-1167143226

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("rajistics/autotrain-auditor-sentiment-1167143226", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("rajistics/autotrain-auditor-sentiment-1167143226", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)
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Dataset used to train FinanceInc/auditor_sentiment_finetuned

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Evaluation results

  • Accuracy on FinanceInc/auditor_sentiment
    verified
    0.862
  • F1 on FinanceInc/auditor_sentiment
    verified
    0.845
  • Recall on FinanceInc/auditor_sentiment
    verified
    0.846
  • Precision on FinanceInc/auditor_sentiment
    verified
    0.844
  • Accuracy on FinanceInc/auditor_sentiment_2021
    verified
    0.849
  • F1 on FinanceInc/auditor_sentiment_2021
    verified
    0.848
  • Recall on FinanceInc/auditor_sentiment_2021
    verified
    0.809
  • Precision on FinanceInc/auditor_sentiment_2021
    verified
    0.819