Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("chrislee973/test-trainer-quant")
model = AutoModelForSequenceClassification.from_pretrained("chrislee973/test-trainer-quant")Quick Links
test-trainer-quant
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6036
- Accuracy: 0.7083
- F1: 0.8221
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 459 | 0.6299 | 0.6838 | 0.8122 |
| 0.6415 | 2.0 | 918 | 0.6096 | 0.7034 | 0.8191 |
| 0.6314 | 3.0 | 1377 | 0.6036 | 0.7083 | 0.8221 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for chrislee973/test-trainer-quant
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chrislee973/test-trainer-quant")