Text Classification
Transformers
PyTorch
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Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-sst2")
model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-sst2")Quick Links
bert-base-uncased-sst2
This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2478
- Accuracy: 0.9323
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: 32
- 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 |
|---|---|---|---|---|
| 0.1668 | 1.0 | 2105 | 0.2513 | 0.9174 |
| 0.1119 | 2.0 | 4210 | 0.2478 | 0.9323 |
| 0.0699 | 3.0 | 6315 | 0.2764 | 0.9266 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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Model tree for JeremiahZ/bert-base-uncased-sst2
Base model
google-bert/bert-base-uncasedDataset used to train JeremiahZ/bert-base-uncased-sst2
Evaluation results
- Accuracy on GLUE SST2self-reported0.932
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-sst2")