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--- |
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language: en |
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tags: |
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- autotrain |
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- DEV |
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datasets: |
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- rajistics/autotrain-data-auditor-sentiment |
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- FinanceInc/auditor_sentiment |
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widget: |
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- text: Operating profit jumped to EUR 47 million from EUR 6.6 million |
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co2_eq_emissions: 3.165771608457648 |
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model-index: |
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- name: auditor_sentiment_finetuned |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: FinanceInc/auditor_sentiment |
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type: glue |
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split: validation |
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metrics: |
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- type: accuracy |
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value: 0.848937 |
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name: Accuracy |
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verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWQ1N2FhNjliMzMyOGVjZWUwYTllMmM5Nzg3ZWYxYzY5NWZkYzQxMmQ3OTI1NjY5MjU3NjdiNzVkNGU5YWZiMCIsInZlcnNpb24iOjF9.W3FtDbi_SgD0kwotQ14wwVsmLor8uYR4vNlW8_MqTY99vw7pZNURkq8VtrGh9nKzGUJTv7vWdX1moIA8rCNEDA |
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- type: f1 |
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value: 0.848282 |
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name: F1 |
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verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWMxY2Q2Nzk0MmM5NzJhNzVhOWYyMDhkMDk1MWJkMjFmOTA2YzUwNjMxNmVlMWI5NjhmOGI0NmQ0MGIyMWRhYSIsInZlcnNpb24iOjF9.HkMmrEUXuzU_jHjMO9g6V1Xo2svOe5gdlu28SyMUXugJbIy5_RJ6joDyhxj06TucT_ZRhr6v77AxCgHB3uwuDA |
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- type: recall |
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value: 0.808937 |
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name: Recall |
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verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODViZDYzOWYzNmQyMjlmYjhlMmExOGY0ZDBjMDFmNWMzYWM0OWVhYWJlNTBkMGEwYTYzY2IyN2Y0MmExZDE1YyIsInZlcnNpb24iOjF9.C1T-yBNPoZ8F-vVYIp9oTd6k4mTSOFw4kAcr6er68Psmt0mfuJ0Xb2nWGXeA0jrgV6bUoomTpZbwGRxtUXzAAA |
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- type: precision |
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value: 0.818542 |
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name: Precision |
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verified: true |
|
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTk3NTIyZDA5MjY1NjZlMjQ0M2ZmNTU3MmRmYzM2NWVhZjU1ZDVkMTU1NTA0MzNkNzIxMjI5ZDAwNjNmNWNjNyIsInZlcnNpb24iOjF9.NBlzUtsAmjG-vBch2KxTNaahGdjFx1IYXWo7AsKQru1kNeVzmoYr-HMixQjgMG2Lg5XW8-yoP79eDOMh_lvLCg |
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- type: accuracy |
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value: 0.848937 |
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name: Accuracy |
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verified: true |
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- type: f1 |
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value: 0.848282 |
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name: F1 |
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verified: true |
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- type: recall |
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value: 0.808937 |
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name: Recall |
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verified: true |
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- type: precision |
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value: 0.818542 |
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name: Precision |
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verified: true |
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--- |
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# Auditor Review Sentiment Model |
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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. |
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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. |
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# Training Data |
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This model was fine-tuned using [Autotrain](https://ui.autotrain.huggingface.co/11671/metrics) from the demo-org/auditor_review review dataset. |
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# Model Status |
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This model is currently being evaluated in development until the end of the quarter. Based on the results, it may be elevated to production. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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- mixed_precision_training: Native AMP |
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# Model Trained Using AutoTrain |
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- Problem type: Multi-class Classification |
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- Model ID: [1167143226](https://huggingface.co/rajistics/autotrain-auditor-sentiment-1167143226) |
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- CO2 Emissions (in grams): 3.165771608457648 |
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## Validation Metrics |
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- Loss: 0.3418470025062561 |
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- Accuracy: 0.8617131062951496 |
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- Macro F1: 0.8448284352912685 |
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- Micro F1: 0.8617131062951496 |
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- Weighted F1: 0.8612696670395574 |
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- Macro Precision: 0.8440532616584138 |
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- Micro Precision: 0.8617131062951496 |
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- Weighted Precision: 0.8612762332366959 |
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- Macro Recall: 0.8461980005490884 |
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- Micro Recall: 0.8617131062951496 |
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- Weighted Recall: 0.8617131062951496 |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ 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 |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("rajistics/autotrain-auditor-sentiment-1167143226", use_auth_token=True) |
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tokenizer = AutoTokenizer.from_pretrained("rajistics/autotrain-auditor-sentiment-1167143226", use_auth_token=True) |
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inputs = tokenizer("I love AutoTrain", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
