
# 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](https://github.com/warwickai/fin-perceiver).

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)

{"language": "en", "license": "apache-2.0", "tags": ["financial-sentiment-analysis", "sentiment-analysis", "language-perceiver"], "datasets": ["financial_phrasebank"], "metrics": ["recall", "f1", "accuracy", "precision"], "widget": [{"text": "INDEX100 fell sharply today."}, {"text": "ImaginaryJetCo bookings hit by Omicron variant as losses total \u00a31bn."}, {"text": "Q1 ImaginaryGame's earnings beat expectations."}, {"text": "Should we buy IMAGINARYSTOCK today?"}], "model-index": [{"name": "fin-perceiver", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type": "financial_phrasebank", "args": "sentences_50agree"}, "metrics": [{"type": "accuracy", "value": 0.8624, "name": "Accuracy"}, {"type": "f1", "value": 0.8416, "name": "F1", "args": "macro"}, {"type": "precision", "value": 0.8438, "name": "Precision", "args": "macro"}, {"type": "recall", "value": 0.8415, "name": "Recall", "args": "macro"}]}]}]}
transformers pytorch safetensors perceiver text-classification financial-sentiment-analysis sentiment-analysis language-perceiver en dataset:financial_phrasebank doi:10.57967/hf/0060 license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #perceiver #text-classification #financial-sentiment-analysis #sentiment-analysis #language-perceiver #en #dataset-financial_phrasebank #doi-10.57967/hf/0060 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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.



The hyperparameters used are as follows.



## Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)

# 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.



The hyperparameters used are as follows. ## Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)
TAGS
#transformers #pytorch #safetensors #perceiver #text-classification #financial-sentiment-analysis #sentiment-analysis #language-perceiver #en #dataset-financial_phrasebank #doi-10.57967/hf/0060 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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.



The hyperparameters used are as follows. ## Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)
passage: TAGS
#transformers #pytorch #safetensors #perceiver #text-classification #financial-sentiment-analysis #sentiment-analysis #language-perceiver #en #dataset-financial_phrasebank #doi-10.57967/hf/0060 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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.



The hyperparameters used are as follows.## Datasets
This model was trained on the Financial PhraseBank (>= 50% agreement)