# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("abk20031218/checkpoints")
model = AutoModelForTokenClassification.from_pretrained("abk20031218/checkpoints", device_map="auto")Quick Links
checkpoints
This model is a fine-tuned version of facebook/esm2_t12_35M_UR50D on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6773
- Accuracy: 0.7253
- Precision: 0.3633
- Recall: 0.4923
- F1: 0.4180
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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.6281 | 1.0 | 1028 | 0.6312 | 0.7622 | 0.4035 | 0.3903 | 0.3968 |
| 0.5836 | 2.0 | 2056 | 0.6297 | 0.7553 | 0.3973 | 0.4273 | 0.4118 |
| 0.5832 | 3.0 | 3084 | 0.6605 | 0.7754 | 0.4326 | 0.3880 | 0.4091 |
| 0.5177 | 4.0 | 4112 | 0.6891 | 0.7604 | 0.4052 | 0.4179 | 0.4114 |
| 0.5016 | 5.0 | 5140 | 0.6773 | 0.7253 | 0.3633 | 0.4923 | 0.4180 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for abk20031218/checkpoints
Base model
facebook/esm2_t12_35M_UR50D
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="abk20031218/checkpoints")