Instructions to use sara-m98/MAMO_GPT_1_DESCARTADO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sara-m98/MAMO_GPT_1_DESCARTADO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sara-m98/MAMO_GPT_1_DESCARTADO")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sara-m98/MAMO_GPT_1_DESCARTADO") model = AutoModelForTokenClassification.from_pretrained("sara-m98/MAMO_GPT_1_DESCARTADO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Epoch Training Loss Validation Loss Precision Recall F1 Accuracy 1 No log 0.442302 0.000000 0.000000 0.000000 0.921068 2 No log 0.325801 0.271818 0.186177 0.220990 0.937482 3 No log 0.275987 0.558767 0.361146 0.438729 0.948194 4 No log 0.266921 0.436055 0.352428 0.389807 0.944998 5 No log 0.252443 0.460766 0.457036 0.458893 0.947859 6 No log 0.247183 0.491369 0.496264 0.493804 0.949358 7 No log 0.244001 0.475631 0.504359 0.489574 0.948371 8 No log 0.246088 0.505109 0.492528 0.498739 0.949950 9 No log 0.248418 0.461495 0.503736 0.481691 0.947464 10 0.359900 0.249883 0.423774 0.495019 0.456634 0.945254 11 0.359900 0.248728 0.415771 0.505604 0.456308 0.945945 12 0.359900 0.256253 0.428571 0.502491 0.462597 0.945097 13 0.359900 0.254979 0.426273 0.495019 0.458081 0.945925 14 0.359900 0.262888 0.436256 0.504981 0.468110 0.946142 15 0.359900 0.263571 0.427807 0.498132 0.460299 0.946616 16 0.359900 0.273070 0.404372 0.460772 0.430733 0.944958 17 0.359900 0.275624 0.376328 0.463263 0.415294 0.942552 18 0.359900 0.276683 0.405005 0.493773 0.445006 0.945018 19 0.110400 0.277764 0.419355 0.493773 0.453532 0.945827 20 0.110400 0.285297 0.372751 0.451432 0.408336 0.941940 21 0.110400 0.286718 0.400929 0.483811 0.438488 0.943400 22 0.110400 0.291095 0.388075 0.462017 0.421831 0.944090 23 0.110400 0.290306 0.413196 0.475716 0.442258 0.945235 24 0.110400 0.291485 0.395931 0.472603 0.430883 0.943617 25 0.110400 0.290052 0.410705 0.496887 0.449704 0.944347 26 0.110400 0.297023 0.401696 0.471980 0.434011 0.944327 27 0.110400 0.300248 0.416359 0.491283 0.450728 0.944820 28 0.110400 0.299196 0.398673 0.486301 0.438149 0.943676 29 0.072000 0.300225 0.414916 0.491905 0.450142 0.944702 30 0.072000 0.302264 0.423574 0.490037 0.454388 0.945274 31 0.072000 0.301968 0.414481 0.491905 0.449886 0.944682 32 0.072000 0.302151 0.416359 0.491283 0.450728 0.944761
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