Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="jinya425/bert-base-cased_for_sentiment_analysis")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jinya425/bert-base-cased_for_sentiment_analysis")
model = AutoModelForSequenceClassification.from_pretrained("jinya425/bert-base-cased_for_sentiment_analysis")Quick Links
bert-base-cased_for_sentiment_analysis
This model is a fine-tuned version of bert-base-cased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.6286
- Accuracy: 0.874
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: 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
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 125 | 0.3699 | 0.847 |
| No log | 2.0 | 250 | 0.4779 | 0.859 |
| No log | 3.0 | 375 | 0.6286 | 0.874 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cpu
- Datasets 2.14.7
- Tokenizers 0.14.1
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Model tree for jinya425/bert-base-cased_for_sentiment_analysis
Base model
google-bert/bert-base-casedDataset used to train jinya425/bert-base-cased_for_sentiment_analysis
Evaluation results
- Accuracy on imdbtest set self-reported0.874
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