readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: mdeberta_all
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # mdeberta_all
  
  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2148
  - Aerospacemanufacturer Precision: 0.7073
  - Aerospacemanufacturer Recall: 0.8406
  - Aerospacemanufacturer F1: 0.7682
  - Aerospacemanufacturer Number: 138
  - Anatomicalstructure Precision: 0.6762
  - Anatomicalstructure Recall: 0.7269
  - Anatomicalstructure F1: 0.7006
  - Anatomicalstructure Number: 227
  - Artwork Precision: 0.5802
  - Artwork Recall: 0.5802
  - Artwork F1: 0.5802
  - Artwork Number: 131
  - Artist Precision: 0.7565
  - Artist Recall: 0.7938
  - Artist F1: 0.7747
  - Artist Number: 1722
  - Athlete Precision: 0.7195
  - Athlete Recall: 0.7636
  - Athlete F1: 0.7409
  - Athlete Number: 719
  - Carmanufacturer Precision: 0.6806
  - Carmanufacturer Recall: 0.8176
  - Carmanufacturer F1: 0.7429
  - Carmanufacturer Number: 159
  - Cleric Precision: 0.6867
  - Cleric Recall: 0.5124
  - Cleric F1: 0.5869
  - Cleric Number: 201
  - Clothing Precision: 0.5797
  - Clothing Recall: 0.625
  - Clothing F1: 0.6015
  - Clothing Number: 128
  - Disease Precision: 0.6262
  - Disease Recall: 0.6768
  - Disease F1: 0.6505
  - Disease Number: 198
  - Drink Precision: 0.7296
  - Drink Recall: 0.8112
  - Drink F1: 0.7682
  - Drink Number: 143
  - Facility Precision: 0.6439
  - Facility Recall: 0.7203
  - Facility F1: 0.6800
  - Facility Number: 497
  - Food Precision: 0.6786
  - Food Recall: 0.5327
  - Food F1: 0.5969
  - Food Number: 214
  - Humansettlement Precision: 0.8594
  - Humansettlement Recall: 0.8792
  - Humansettlement F1: 0.8692
  - Humansettlement Number: 1689
  - Medicalprocedure Precision: 0.6545
  - Medicalprocedure Recall: 0.7606
  - Medicalprocedure F1: 0.7036
  - Medicalprocedure Number: 142
  - Medication/vaccine Precision: 0.7183
  - Medication/vaccine Recall: 0.765
  - Medication/vaccine F1: 0.7409
  - Medication/vaccine Number: 200
  - Musicalgrp Precision: 0.7132
  - Musicalgrp Recall: 0.7688
  - Musicalgrp F1: 0.7400
  - Musicalgrp Number: 372
  - Musicalwork Precision: 0.7513
  - Musicalwork Recall: 0.7052
  - Musicalwork F1: 0.7275
  - Musicalwork Number: 407
  - Org Precision: 0.6335
  - Org Recall: 0.6117
  - Org F1: 0.6224
  - Org Number: 667
  - Otherloc Precision: 0.7514
  - Otherloc Recall: 0.6205
  - Otherloc F1: 0.6797
  - Otherloc Number: 224
  - Otherper Precision: 0.4558
  - Otherper Recall: 0.5821
  - Otherper F1: 0.5112
  - Otherper Number: 859
  - Otherprod Precision: 0.6076
  - Otherprod Recall: 0.5543
  - Otherprod F1: 0.5797
  - Otherprod Number: 433
  - Politician Precision: 0.6228
  - Politician Recall: 0.4793
  - Politician F1: 0.5417
  - Politician Number: 603
  - Privatecorp Precision: 0.7159
  - Privatecorp Recall: 0.4884
  - Privatecorp F1: 0.5806
  - Privatecorp Number: 129
  - Publiccorp Precision: 0.56
  - Publiccorp Recall: 0.6914
  - Publiccorp F1: 0.6188
  - Publiccorp Number: 243
  - Scientist Precision: 0.4545
  - Scientist Recall: 0.4497
  - Scientist F1: 0.4521
  - Scientist Number: 189
  - Software Precision: 0.7159
  - Software Recall: 0.8046
  - Software F1: 0.7577
  - Software Number: 307
  - Sportsgrp Precision: 0.7845
  - Sportsgrp Recall: 0.8701
  - Sportsgrp F1: 0.8251
  - Sportsgrp Number: 385
  - Sportsmanager Precision: 0.6667
  - Sportsmanager Recall: 0.5361
  - Sportsmanager F1: 0.5943
  - Sportsmanager Number: 194
  - Station Precision: 0.7406
  - Station Recall: 0.8093
  - Station F1: 0.7734
  - Station Number: 194
  - Symptom Precision: 0.6316
  - Symptom Recall: 0.5581
  - Symptom F1: 0.5926
  - Symptom Number: 129
  - Vehicle Precision: 0.5514
  - Vehicle Recall: 0.6505
  - Vehicle F1: 0.5969
  - Vehicle Number: 206
  - Visualwork Precision: 0.7538
  - Visualwork Recall: 0.7951
  - Visualwork F1: 0.7739
  - Visualwork Number: 693
  - Writtenwork Precision: 0.6913
  - Writtenwork Recall: 0.6803
  - Writtenwork F1: 0.6858
  - Writtenwork Number: 563
  - Overall Precision: 0.6928
  - Overall Recall: 0.7142
  - Overall F1: 0.7033
  - Overall Accuracy: 0.9355
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 15.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Aerospacemanufacturer Precision | Aerospacemanufacturer Recall | Aerospacemanufacturer F1 | Aerospacemanufacturer Number | Anatomicalstructure Precision | Anatomicalstructure Recall | Anatomicalstructure F1 | Anatomicalstructure Number | Artwork Precision | Artwork Recall | Artwork F1 | Artwork Number | Artist Pre