Instructions to use aehrm/dtaec-type-normalizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrm/dtaec-type-normalizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="aehrm/dtaec-type-normalizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aehrm/dtaec-type-normalizer") model = AutoModelForSeq2SeqLM.from_pretrained("aehrm/dtaec-type-normalizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 20.0, | |
| "eval_gen_len": 12.35156514, | |
| "eval_loss": 0.030767865478992462, | |
| "eval_runtime": 534.9614, | |
| "eval_samples": 53222, | |
| "eval_samples_per_second": 99.488, | |
| "eval_steps_per_second": 1.555, | |
| "eval_wordacc": 0.95458645, | |
| "eval_wordacc_oov": 0.90963293 | |
| } |