Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("zwellington/microtest")
model = AutoModelForSequenceClassification.from_pretrained("zwellington/microtest")Quick Links
microtest
This model is a fine-tuned version of bert-base-uncased on the azaheadhealth dataset. It achieves the following results on the evaluation set:
- Loss: 0.6111
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.5955 | 0.5 | 1 | 0.6676 | 0.5 | 0.5 | 0.5 | 0.5 |
| 0.633 | 1.0 | 2 | 0.6111 | 1.0 | 1.0 | 1.0 | 1.0 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.2
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Model tree for zwellington/microtest
Base model
google-bert/bert-base-uncasedEvaluation results
- Accuracy on azaheadhealthtest set self-reported1.000
- F1 on azaheadhealthtest set self-reported1.000
- Precision on azaheadhealthtest set self-reported1.000
- Recall on azaheadhealthtest set self-reported1.000
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zwellington/microtest")