Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Adoley/covid-tweets-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("Adoley/covid-tweets-sentiment-analysis")Quick Links
covid-tweets-sentiment-analysis
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6091
- Rmse: 0.6632
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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- 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 | Rmse |
|---|---|---|---|---|
| 0.7648 | 2.0 | 500 | 0.6091 | 0.6632 |
| 0.4033 | 4.0 | 1000 | 0.7708 | 0.6632 |
| 0.1444 | 6.0 | 1500 | 1.0443 | 0.6563 |
| 0.0625 | 8.0 | 2000 | 1.3089 | 0.6628 |
| 0.0324 | 10.0 | 2500 | 1.3869 | 0.6673 |
Framework versions
- Transformers 4.29.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 4
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Adoley/covid-tweets-sentiment-analysis")