Text Classification
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roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mahwizzzz/UrduSentimentClassification")
model = AutoModelForSequenceClassification.from_pretrained("mahwizzzz/UrduSentimentClassification")Quick Links
UrduSentimentClassification
This model is a fine-tuned version of urduhack/roberta-urdu-small on Financial dataset. It achieves the following results on the evaluation set:
- Loss: 0.4924
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4931 | 1.96 | 500 | 0.5585 |
| 0.2415 | 3.92 | 1000 | 0.3782 |
| 0.1116 | 5.88 | 1500 | 0.6486 |
| 0.0357 | 7.84 | 2000 | 0.4853 |
| 0.0101 | 9.8 | 2500 | 0.4924 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for mahwizzzz/UrduSentimentClassification
Base model
urduhack/roberta-urdu-small
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mahwizzzz/UrduSentimentClassification")