# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gustavecortal/roberta-tec")
model = AutoModelForSequenceClassification.from_pretrained("gustavecortal/roberta-tec")Quick Links
roberta_tec_gpu_v1
This model is a fine-tuned version of ibm/ColD-Fusion on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2970
- F1: 0.8202
- Roc Auc: 0.8806
- Recall: 0.8561
- Precision: 0.7871
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.4549 | 1.0 | 923 | 0.3128 | 0.7604 | 0.8277 | 0.7404 | 0.7815 |
| 0.251 | 2.0 | 1846 | 0.2970 | 0.8202 | 0.8806 | 0.8561 | 0.7871 |
| 0.1509 | 3.0 | 2769 | 0.3228 | 0.8146 | 0.8713 | 0.8246 | 0.8048 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gustavecortal/roberta-tec")