Instructions to use scott156/LEDBaseNSPCCV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scott156/LEDBaseNSPCCV1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("scott156/LEDBaseNSPCCV1") model = AutoModelForSeq2SeqLM.from_pretrained("scott156/LEDBaseNSPCCV1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from scott156/LEDBaseNSPCCV1: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
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https://huggingface.co/scott156/LEDBaseNSPCCV1/resolve/main/README.md
- Command line
-
hf download hf://scott156/LEDBaseNSPCCV1/README.md
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curl -L -o README.md https://huggingface.co/scott156/LEDBaseNSPCCV1/resolve/main/README.md
1.79 kB
metadata
license: apache-2.0
base_model: allenai/led-base-16384
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: LED-Base-NSPCC
results: []
LED-Base-NSPCC
This model is a fine-tuned version of allenai/led-base-16384 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8152
- Rouge1: 0.4955
- Rouge2: 0.2131
- Rougel: 0.2804
- Rougelsum: 0.2807
- Gen Len: 267.3511
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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.5412 | 0.99 | 47 | 1.9338 | 0.4778 | 0.186 | 0.2638 | 0.2635 | 266.2766 |
| 1.6145 | 1.99 | 94 | 1.8152 | 0.4955 | 0.2131 | 0.2804 | 0.2807 | 267.3511 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2