Instructions to use dddb/title_generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dddb/title_generator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dddb/title_generator") model = AutoModelForSeq2SeqLM.from_pretrained("dddb/title_generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
tags: autotrain
language: unk
widget:
- text: I love AutoTrain 🤗
datasets:
- dddb/autotrain-data-mt5_chinese_small_finetune
co2_eq_emissions: 0.2263611804615655
Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1060836848
- CO2 Emissions (in grams): 0.2263611804615655
Validation Metrics
- Loss: 2.3939340114593506
- Rouge1: 0.3375
- Rouge2: 0.0
- RougeL: 0.3375
- RougeLsum: 0.3375
- Gen Len: 11.4395
Usage
You can use cURL to access this model:
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/dddb/autotrain-mt5_chinese_small_finetune-1060836848