metadata
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_clf_results
results: []
datasets:
- app_reviews
language:
- en
library_name: transformers
pipeline_tag: text-classification
bert_clf_results
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9611
- Accuracy: 0.7011
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0767 | 1.0 | 5401 | 0.8447 | 0.7087 |
| 0.6523 | 2.0 | 10803 | 0.8287 | 0.7156 |
| 0.7209 | 3.0 | 16204 | 0.8852 | 0.7121 |
| 0.4274 | 4.0 | 21604 | 0.9611 | 0.7011 |
Code Implementation
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Andyrasika/bert_clf_results")
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForSequenceClassification.from_pretrained("Andyrasika/bert_clf_results")
with torch.no_grad():
logits = model(**inputs).logits
predicted_class_id = logits.argmax().item()
model.config.id2label[predicted_class_id]
Output
'LABEL_4'
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0