Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("GhylB/Sentiment_Analysis_BERT_Based_MODEL")
model = AutoModelForSequenceClassification.from_pretrained("GhylB/Sentiment_Analysis_BERT_Based_MODEL")Quick Links
Sentiment_Analysis_BERT_Based_MODEL
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: 0.5955
- Rmse: 0.6695
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|---|---|---|---|---|
| 0.7508 | 2.0 | 500 | 0.5955 | 0.6695 |
| 0.3953 | 4.0 | 1000 | 0.7485 | 0.6605 |
| 0.1399 | 6.0 | 1500 | 1.0561 | 0.6703 |
| 0.0585 | 8.0 | 2000 | 1.3094 | 0.6525 |
| 0.0298 | 10.0 | 2500 | 1.4381 | 0.6673 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GhylB/Sentiment_Analysis_BERT_Based_MODEL")