Text Classification
Transformers
TensorBoard
Safetensors
camembert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("djamina/relatives_labels")
model = AutoModelForSequenceClassification.from_pretrained("djamina/relatives_labels")Quick Links
relatives_labels
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6840
- Accuracy: 0.5758
- F1: 0.7042
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 49 | 0.8393 | 0.4141 | 0.0 |
| No log | 2.0 | 98 | 0.7570 | 0.5859 | 0.7389 |
| No log | 3.0 | 147 | 0.6867 | 0.5859 | 0.7389 |
| No log | 4.0 | 196 | 0.6835 | 0.5859 | 0.7389 |
| No log | 5.0 | 245 | 0.6840 | 0.5758 | 0.7042 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="djamina/relatives_labels")