# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Feluda/Final_Fine_Tuned_Legal_Led")
model = AutoModelForSeq2SeqLM.from_pretrained("Feluda/Final_Fine_Tuned_Legal_Led")Quick Links
results
This model is a fine-tuned version of nsi319/legal-led-base-16384 on the joelniklaus/legal_case_document_summarization dataset. It achieves the following results on the evaluation set:
- Loss: 2.7401
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.2 | 1.0 | 1924 | 2.8550 |
| 3.6193 | 2.0 | 3848 | 2.7593 |
| 2.7776 | 3.0 | 5772 | 2.7401 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
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Model tree for Feluda/Final_Fine_Tuned_Legal_Led
Base model
nsi319/legal-led-base-16384
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Feluda/Final_Fine_Tuned_Legal_Led")