Summarization
Transformers
PyTorch
Safetensors
English
led
text2text-generation
Eval Results (legacy)
Instructions to use AlgorithmicResearchGroup/led_base_16384_billsum_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlgorithmicResearchGroup/led_base_16384_billsum_summarization with Transformers:
# 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="AlgorithmicResearchGroup/led_base_16384_billsum_summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AlgorithmicResearchGroup/led_base_16384_billsum_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("AlgorithmicResearchGroup/led_base_16384_billsum_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,061 Bytes
4fff009 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"best_metric": null,
"best_model_checkpoint": null,
"epoch": 2.702406078514141,
"global_step": 1600,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.42,
"learning_rate": 8.333333333333334e-06,
"loss": 1.7293,
"step": 250
},
{
"epoch": 0.84,
"learning_rate": 1.6666666666666667e-05,
"loss": 1.2912,
"step": 500
},
{
"epoch": 1.27,
"learning_rate": 2.5e-05,
"loss": 1.1918,
"step": 750
},
{
"epoch": 1.69,
"learning_rate": 3.3333333333333335e-05,
"loss": 1.1441,
"step": 1000
},
{
"epoch": 2.11,
"learning_rate": 4.166666666666667e-05,
"loss": 1.1157,
"step": 1250
},
{
"epoch": 2.53,
"learning_rate": 5e-05,
"loss": 1.0748,
"step": 1500
}
],
"max_steps": 1776,
"num_train_epochs": 3,
"total_flos": 9.830437283000484e+17,
"trial_name": null,
"trial_params": null
}
|