readme: ---
  language: en
  tags:
  - autotrain
  - DEV
  datasets:
  - rajistics/autotrain-data-auditor-sentiment
  - FinanceInc/auditor_sentiment
  widget:
  - text: Operating profit jumped to EUR 47 million from EUR 6.6 million
  co2_eq_emissions: 3.165771608457648
  model-index:
  - name: auditor_sentiment_finetuned
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: FinanceInc/auditor_sentiment
        type: glue
        split: validation
      metrics:
      - type: accuracy
        value: 0.848937
        name: Accuracy
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWQ1N2FhNjliMzMyOGVjZWUwYTllMmM5Nzg3ZWYxYzY5NWZkYzQxMmQ3OTI1NjY5MjU3NjdiNzVkNGU5YWZiMCIsInZlcnNpb24iOjF9.W3FtDbi_SgD0kwotQ14wwVsmLor8uYR4vNlW8_MqTY99vw7pZNURkq8VtrGh9nKzGUJTv7vWdX1moIA8rCNEDA
      - type: f1
        value: 0.848282
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWMxY2Q2Nzk0MmM5NzJhNzVhOWYyMDhkMDk1MWJkMjFmOTA2YzUwNjMxNmVlMWI5NjhmOGI0NmQ0MGIyMWRhYSIsInZlcnNpb24iOjF9.HkMmrEUXuzU_jHjMO9g6V1Xo2svOe5gdlu28SyMUXugJbIy5_RJ6joDyhxj06TucT_ZRhr6v77AxCgHB3uwuDA
      - type: recall
        value: 0.808937
        name: Recall
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODViZDYzOWYzNmQyMjlmYjhlMmExOGY0ZDBjMDFmNWMzYWM0OWVhYWJlNTBkMGEwYTYzY2IyN2Y0MmExZDE1YyIsInZlcnNpb24iOjF9.C1T-yBNPoZ8F-vVYIp9oTd6k4mTSOFw4kAcr6er68Psmt0mfuJ0Xb2nWGXeA0jrgV6bUoomTpZbwGRxtUXzAAA
      - type: precision
        value: 0.818542
        name: Precision
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTk3NTIyZDA5MjY1NjZlMjQ0M2ZmNTU3MmRmYzM2NWVhZjU1ZDVkMTU1NTA0MzNkNzIxMjI5ZDAwNjNmNWNjNyIsInZlcnNpb24iOjF9.NBlzUtsAmjG-vBch2KxTNaahGdjFx1IYXWo7AsKQru1kNeVzmoYr-HMixQjgMG2Lg5XW8-yoP79eDOMh_lvLCg
      - type: accuracy
        value: 0.848937
        name: Accuracy
        verified: true
      - type: f1
        value: 0.848282
        name: F1
        verified: true
      - type: recall
        value: 0.808937
        name: Recall
        verified: true
      - type: precision
        value: 0.818542
        name: Precision
        verified: true
  ---
  
  # Auditor Review Sentiment Model
  
  This model has been finetuned from the proprietary version of [FinBERT](https://huggingface.co/FinanceInc/finbert-pretrain) 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](https://ui.autotrain.huggingface.co/11671/metrics) 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](https://huggingface.co/rajistics/autotrain-auditor-sentiment-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)
  ```