Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hacktrix-121/bert-base-uncased-opp115-multilabel")
model = AutoModelForSequenceClassification.from_pretrained("Hacktrix-121/bert-base-uncased-opp115-multilabel")Quick Links
bert-base-uncased-opp115-multilabel
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: 0.1271
- Accuracy: 0.9664
- F1: 0.5920
- Precision: 0.6890
- Recall: 0.5365
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 274 | 0.1607 | 0.9538 | 0.4371 | 0.6079 | 0.3670 |
| 0.1656 | 2.0 | 548 | 0.1327 | 0.9618 | 0.5292 | 0.6972 | 0.4585 |
| 0.1656 | 3.0 | 822 | 0.1271 | 0.9664 | 0.5920 | 0.6890 | 0.5365 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for Hacktrix-121/bert-base-uncased-opp115-multilabel
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hacktrix-121/bert-base-uncased-opp115-multilabel")