Instructions to use eclec/patentClassificationLongFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eclec/patentClassificationLongFormer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eclec/patentClassificationLongFormer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eclec/patentClassificationLongFormer") model = AutoModelForSequenceClassification.from_pretrained("eclec/patentClassificationLongFormer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
patentClassificationLongFormer
This model is a fine-tuned version of allenai/longformer-large-4096 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6186
- Accuracy: 0.6753
- F1: 0.6783
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: 1.4489735300181872e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 240
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.666 | 1.0 | 4438 | 0.6336 | 0.6491 | 0.6802 |
| 0.6063 | 2.0 | 8876 | 0.6186 | 0.6753 | 0.6783 |
| 0.5543 | 3.0 | 13314 | 0.6192 | 0.6762 | 0.6908 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for eclec/patentClassificationLongFormer
Base model
allenai/longformer-large-4096