metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- recall
- accuracy
- precision
base_model: deepmind/language-perceiver
model-index:
- name: financial_sentiment_model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
args: sentences_50agree
metrics:
- type: recall
value: 0.8839956357328868
name: Recall
- type: accuracy
value: 0.8804123711340206
name: Accuracy
- type: precision
value: 0.8604175202419276
name: Precision
financial_sentiment_model
This model is a fine-tuned version of deepmind/language-perceiver on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.3467
- Recall: 0.8840
- Accuracy: 0.8804
- Precision: 0.8604
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Accuracy | Precision |
|---|---|---|---|---|---|---|
| 0.4481 | 1.0 | 273 | 0.4035 | 0.8526 | 0.8433 | 0.7955 |
| 0.4069 | 2.0 | 546 | 0.4478 | 0.8683 | 0.8289 | 0.8123 |
| 0.2225 | 3.0 | 819 | 0.3167 | 0.8747 | 0.8680 | 0.8387 |
| 0.1245 | 4.0 | 1092 | 0.3467 | 0.8840 | 0.8804 | 0.8604 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.9.0+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3