Instructions to use dpravinv/fold_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpravinv/fold_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpravinv/fold_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpravinv/fold_2") model = AutoModelForSequenceClassification.from_pretrained("dpravinv/fold_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,003 Bytes
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library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fold_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fold_2
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1221
- F1: 0.8632
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4106 | 1.0 | 300 | 0.3857 | 0.8353 |
| 0.2705 | 2.0 | 600 | 0.3586 | 0.8615 |
| 0.1633 | 3.0 | 900 | 0.4721 | 0.8439 |
| 0.0804 | 4.0 | 1200 | 0.5829 | 0.8503 |
| 0.052 | 5.0 | 1500 | 0.7545 | 0.8536 |
| 0.0245 | 6.0 | 1800 | 1.0105 | 0.8581 |
| 0.0119 | 7.0 | 2100 | 0.9987 | 0.8609 |
| 0.006 | 8.0 | 2400 | 1.1393 | 0.8603 |
| 0.0041 | 9.0 | 2700 | 1.1023 | 0.8644 |
| 0.0015 | 10.0 | 3000 | 1.1221 | 0.8632 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
|