Instructions to use scott156/LEDLargeNSPCCV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scott156/LEDLargeNSPCCV1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("scott156/LEDLargeNSPCCV1") model = AutoModelForSeq2SeqLM.from_pretrained("scott156/LEDLargeNSPCCV1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from scott156/LEDLargeNSPCCV1: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
-
https://huggingface.co/scott156/LEDLargeNSPCCV1/resolve/main/README.md
- Command line
-
hf download hf://scott156/LEDLargeNSPCCV1/README.md
-
curl -L -o README.md https://huggingface.co/scott156/LEDLargeNSPCCV1/resolve/main/README.md
1.79 kB
metadata
license: apache-2.0
base_model: allenai/led-large-16384
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: LED-Large-NSPCC
results: []
LED-Large-NSPCC
This model is a fine-tuned version of allenai/led-large-16384 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6129
- Rouge1: 0.5117
- Rouge2: 0.2276
- Rougel: 0.2877
- Rougelsum: 0.2864
- Gen Len: 317.0532
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|---|
| 2.2026 | 0.99 | 94 | 1.8032 | 0.3532 | 0.1387 | 0.1944 | 0.1935 | 189.117 |
| 1.3273 | 1.99 | 188 | 1.6129 | 0.5117 | 0.2276 | 0.2877 | 0.2864 | 317.0532 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2