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
| 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 | |
| ``` |