# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("djamina/relatives_psr_seq-cbert_finetuned")
model = AutoModelForTokenClassification.from_pretrained("djamina/relatives_psr_seq-cbert_finetuned")Quick Links
relatives_psr_seq-cbert_finetuned
This model is a fine-tuned version of camembert/camembert-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7099
- Precision: 0.6914
- Recall: 0.2252
- F1: 0.2193
- Accuracy: 0.7578
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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 49 | 0.8241 | 0.9512 | 0.2 | 0.1722 | 0.7560 |
| No log | 2.0 | 98 | 0.8026 | 0.8243 | 0.2100 | 0.1933 | 0.7555 |
| No log | 3.0 | 147 | 0.7535 | 0.8077 | 0.2045 | 0.1823 | 0.7563 |
| No log | 4.0 | 196 | 0.7228 | 0.8227 | 0.2220 | 0.2109 | 0.7586 |
| No log | 5.0 | 245 | 0.7099 | 0.6914 | 0.2252 | 0.2193 | 0.7578 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for djamina/relatives_psr_seq-cbert_finetuned
Base model
almanach/camembert-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="djamina/relatives_psr_seq-cbert_finetuned")