Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Utkarsh03/hb_3")
model = AutoModelForSequenceClassification.from_pretrained("Utkarsh03/hb_3")Quick Links
hb_3
This model is a fine-tuned version of samhitmantrala/hb_2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0446
- Accuracy: 1.0
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 22 | 0.2864 | 1.0 |
| No log | 2.0 | 44 | 0.1302 | 1.0 |
| No log | 3.0 | 66 | 0.0446 | 1.0 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for Utkarsh03/hb_3
Base model
Hate-speech-CNERG/hindi-abusive-MuRIL Finetuned
samhitmantrala/hb_2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Utkarsh03/hb_3")