Instructions to use sara-m98/MAMO_GPT_2_DESCARTADO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sara-m98/MAMO_GPT_2_DESCARTADO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sara-m98/MAMO_GPT_2_DESCARTADO")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sara-m98/MAMO_GPT_2_DESCARTADO") model = AutoModelForTokenClassification.from_pretrained("sara-m98/MAMO_GPT_2_DESCARTADO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Epoch Training Loss Validation Loss Precision Recall F1 Accuracy 1 3.918500 2.066817 0.000000 0.000000 0.000000 0.808381 2 0.462200 0.435293 0.216000 0.067248 0.102564 0.925704 3 0.368800 0.315322 0.342670 0.228518 0.274188 0.942177 4 0.265200 0.265912 0.438281 0.349315 0.388773 0.944820 5 0.235500 0.252256 0.442065 0.437111 0.439574 0.944584 6 0.197800 0.240639 0.461665 0.524907 0.491259 0.948312 7 0.159100 0.240741 0.477299 0.510585 0.493381 0.949003 8 0.144200 0.243670 0.458184 0.556040 0.502391 0.948687 9 0.115800 0.261192 0.397163 0.488169 0.437989 0.940500 10 0.110500 0.260426 0.443164 0.526775 0.481366 0.945886 11 0.081600 0.268203 0.429551 0.506849 0.465010 0.945294 12 0.086100 0.270014 0.412714 0.525529 0.462339 0.942670 13 0.082900 0.291830 0.400000 0.465753 0.430380 0.942374 14 0.069000 0.305032 0.420000 0.470735 0.443922 0.943282 15 0.066700 0.302393 0.431831 0.483188 0.456068 0.944446 16 0.066800 0.307584 0.423448 0.492528 0.455383 0.944150 17 0.061800 0.319186 0.428113 0.498755 0.460742 0.944012 18 0.054400 0.325527 0.433351 0.508095 0.467756 0.945077 19 0.054700 0.327999 0.415005 0.492528 0.450456 0.943617 20 0.043400 0.344858 0.415053 0.466999 0.439496 0.942946 21 0.041500 0.337979 0.449002 0.518057 0.481064 0.945156 22 0.035000 0.355671 0.431124 0.465753 0.447770 0.944327 23 0.035000 0.345297 0.427890 0.502491 0.462199 0.943637 24 0.040900 0.349494 0.440108 0.505604 0.470588 0.944110 25 0.045000 0.350966 0.422572 0.501245 0.458559 0.943558 26 0.034900 0.361274 0.426139 0.483188 0.452874 0.943400 27 0.036000 0.366199 0.432967 0.490660 0.460012 0.943578 28 0.037900 0.363155 0.424422 0.491283 0.455411 0.943341 29 0.037500 0.364137 0.443231 0.505604 0.472368 0.945254 30 0.031700 0.369087 0.440178 0.492528 0.464884 0.944505 31 0.034800 0.367538 0.437637 0.498132 0.465929 0.944623
- Downloads last month
- 3