Text Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("satendrakumar/covid_miss_information_classification2")
model = AutoModelForSequenceClassification.from_pretrained("satendrakumar/covid_miss_information_classification2")Quick Links
covid_miss_information_classification2
This model is a fine-tuned version of distilbert-base-uncased on an tweets dataset. It achieves the following results on the evaluation set:
- Loss: 0.0174
- Accuracy: 0.9966
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1244 | 1.0 | 1132 | 0.0497 | 0.9861 |
| 0.0587 | 2.0 | 2264 | 0.0383 | 0.9906 |
| 0.0244 | 3.0 | 3396 | 0.0213 | 0.9951 |
| 0.0046 | 4.0 | 4528 | 0.0202 | 0.9966 |
| 0.0047 | 5.0 | 5660 | 0.0174 | 0.9966 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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Model tree for satendrakumar/covid_miss_information_classification2
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="satendrakumar/covid_miss_information_classification2")