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scenario-non-kd-po-ner-full-mdeberta_data-univner_en44

Token Classification
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Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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  1. README.md +31 -31
  2. config.json +2 -2
  3. eval_result_ner.json +1 -1
  4. model.safetensors +2 -2
  5. training_args.bin +1 -1
README.md CHANGED
@@ -1,14 +1,14 @@
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  ---
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- base_model: haryoaw/scenario-TCR-NER_data-univner_en
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  library_name: transformers
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  license: mit
 
 
 
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  metrics:
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  - precision
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  - recall
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  - f1
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  - accuracy
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- tags:
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- - generated_from_trainer
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  model-index:
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  - name: scenario-non-kd-po-ner-full-mdeberta_data-univner_en44
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  results: []
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1418
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- - Precision: 0.8347
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- - Recall: 0.8416
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- - F1: 0.8381
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- - Accuracy: 0.9853
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  ## Model description
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@@ -56,29 +56,29 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0023 | 1.2755 | 500 | 0.1132 | 0.8279 | 0.8364 | 0.8321 | 0.9857 |
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- | 0.0022 | 2.5510 | 1000 | 0.1035 | 0.8234 | 0.8540 | 0.8384 | 0.9858 |
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- | 0.0021 | 3.8265 | 1500 | 0.0987 | 0.8228 | 0.8509 | 0.8366 | 0.9851 |
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- | 0.0013 | 5.1020 | 2000 | 0.1237 | 0.7955 | 0.8458 | 0.8199 | 0.9839 |
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- | 0.0008 | 6.3776 | 2500 | 0.1278 | 0.8173 | 0.8292 | 0.8232 | 0.9845 |
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- | 0.0007 | 7.6531 | 3000 | 0.1257 | 0.8257 | 0.8437 | 0.8346 | 0.9848 |
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- | 0.0006 | 8.9286 | 3500 | 0.1257 | 0.8466 | 0.8282 | 0.8373 | 0.9855 |
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- | 0.0011 | 10.2041 | 4000 | 0.1250 | 0.8141 | 0.8251 | 0.8195 | 0.9843 |
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- | 0.0007 | 11.4796 | 4500 | 0.1240 | 0.8206 | 0.8240 | 0.8223 | 0.9840 |
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- | 0.0004 | 12.7551 | 5000 | 0.1297 | 0.8192 | 0.8395 | 0.8292 | 0.9847 |
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- | 0.0008 | 14.0306 | 5500 | 0.1342 | 0.8270 | 0.8116 | 0.8192 | 0.9844 |
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- | 0.0004 | 15.3061 | 6000 | 0.1295 | 0.8147 | 0.8240 | 0.8194 | 0.9843 |
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- | 0.0004 | 16.5816 | 6500 | 0.1374 | 0.8118 | 0.8437 | 0.8274 | 0.9839 |
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- | 0.0003 | 17.8571 | 7000 | 0.1416 | 0.8092 | 0.8209 | 0.8150 | 0.9837 |
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- | 0.0003 | 19.1327 | 7500 | 0.1264 | 0.8249 | 0.8489 | 0.8367 | 0.9852 |
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- | 0.0002 | 20.4082 | 8000 | 0.1323 | 0.8262 | 0.8416 | 0.8338 | 0.9854 |
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- | 0.0003 | 21.6837 | 8500 | 0.1341 | 0.8239 | 0.8427 | 0.8332 | 0.9854 |
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- | 0.0001 | 22.9592 | 9000 | 0.1400 | 0.8251 | 0.8499 | 0.8373 | 0.9852 |
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- | 0.0002 | 24.2347 | 9500 | 0.1342 | 0.8219 | 0.8406 | 0.8311 | 0.9849 |
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- | 0.0002 | 25.5102 | 10000 | 0.1355 | 0.8352 | 0.8447 | 0.8399 | 0.9855 |
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- | 0.0001 | 26.7857 | 10500 | 0.1454 | 0.8254 | 0.8416 | 0.8334 | 0.9846 |
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- | 0.0001 | 28.0612 | 11000 | 0.1448 | 0.8254 | 0.8416 | 0.8334 | 0.9849 |
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- | 0.0001 | 29.3367 | 11500 | 0.1418 | 0.8347 | 0.8416 | 0.8381 | 0.9853 |
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  ### Framework versions
 
1
  ---
 
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  library_name: transformers
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  license: mit
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+ base_model: haryoaw/scenario-TCR-NER_data-univner_en
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+ tags:
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+ - generated_from_trainer
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  metrics:
8
  - precision
9
  - recall
10
  - f1
11
  - accuracy
 
 
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  model-index:
13
  - name: scenario-non-kd-po-ner-full-mdeberta_data-univner_en44
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  results: []
 
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  This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1841
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+ - Precision: 0.7385
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+ - Recall: 0.7453
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+ - F1: 0.7419
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+ - Accuracy: 0.9788
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.1167 | 1.2755 | 500 | 0.1161 | 0.5076 | 0.6573 | 0.5728 | 0.9647 |
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+ | 0.0436 | 2.5510 | 1000 | 0.0883 | 0.6536 | 0.7422 | 0.6951 | 0.9754 |
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+ | 0.0227 | 3.8265 | 1500 | 0.1011 | 0.6709 | 0.7153 | 0.6924 | 0.9767 |
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+ | 0.0129 | 5.1020 | 2000 | 0.1175 | 0.6537 | 0.7505 | 0.6988 | 0.9757 |
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+ | 0.008 | 6.3776 | 2500 | 0.1207 | 0.7106 | 0.7143 | 0.7124 | 0.9774 |
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+ | 0.006 | 7.6531 | 3000 | 0.1357 | 0.6951 | 0.7340 | 0.7140 | 0.9773 |
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+ | 0.004 | 8.9286 | 3500 | 0.1415 | 0.7146 | 0.7153 | 0.7150 | 0.9778 |
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+ | 0.0032 | 10.2041 | 4000 | 0.1439 | 0.7198 | 0.7391 | 0.7293 | 0.9784 |
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+ | 0.0027 | 11.4796 | 4500 | 0.1432 | 0.7150 | 0.7609 | 0.7372 | 0.9780 |
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+ | 0.0022 | 12.7551 | 5000 | 0.1500 | 0.7205 | 0.7578 | 0.7386 | 0.9788 |
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+ | 0.0017 | 14.0306 | 5500 | 0.1416 | 0.7129 | 0.7453 | 0.7287 | 0.9781 |
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+ | 0.0015 | 15.3061 | 6000 | 0.1543 | 0.7164 | 0.7402 | 0.7281 | 0.9780 |
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+ | 0.0013 | 16.5816 | 6500 | 0.1558 | 0.6989 | 0.7640 | 0.7300 | 0.9779 |
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+ | 0.0009 | 17.8571 | 7000 | 0.1592 | 0.7019 | 0.7433 | 0.7220 | 0.9778 |
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+ | 0.0007 | 19.1327 | 7500 | 0.1642 | 0.7227 | 0.7391 | 0.7308 | 0.9785 |
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+ | 0.0007 | 20.4082 | 8000 | 0.1614 | 0.7316 | 0.7422 | 0.7369 | 0.9791 |
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+ | 0.0006 | 21.6837 | 8500 | 0.1739 | 0.7179 | 0.7402 | 0.7288 | 0.9781 |
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+ | 0.0004 | 22.9592 | 9000 | 0.1742 | 0.7260 | 0.7350 | 0.7305 | 0.9783 |
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+ | 0.0004 | 24.2347 | 9500 | 0.1701 | 0.7154 | 0.7547 | 0.7345 | 0.9781 |
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+ | 0.0003 | 25.5102 | 10000 | 0.1795 | 0.7354 | 0.7422 | 0.7388 | 0.9790 |
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+ | 0.0003 | 26.7857 | 10500 | 0.1825 | 0.7278 | 0.7391 | 0.7334 | 0.9785 |
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+ | 0.0002 | 28.0612 | 11000 | 0.1839 | 0.7384 | 0.7391 | 0.7387 | 0.9786 |
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+ | 0.0003 | 29.3367 | 11500 | 0.1841 | 0.7385 | 0.7453 | 0.7419 | 0.9788 |
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  ### Framework versions
config.json CHANGED
@@ -1,7 +1,7 @@
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  {
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  "_name_or_path": "haryoaw/scenario-TCR-NER_data-univner_en",
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  "architectures": [
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- "DebertaV2ForTokenClassification"
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  ],
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  "attention_probs_dropout_prob": 0.1,
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  "hidden_act": "gelu",
@@ -33,7 +33,7 @@
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  "model_type": "deberta-v2",
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  "norm_rel_ebd": "layer_norm",
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  "num_attention_heads": 12,
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- "num_hidden_layers": 12,
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  "pad_token_id": 0,
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  "pooler_dropout": 0,
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  "pooler_hidden_act": "gelu",
 
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  {
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  "_name_or_path": "haryoaw/scenario-TCR-NER_data-univner_en",
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  "architectures": [
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+ "DebertaForTokenClassification"
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  ],
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  "attention_probs_dropout_prob": 0.1,
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  "hidden_act": "gelu",
 
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  "model_type": "deberta-v2",
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  "norm_rel_ebd": "layer_norm",
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  "num_attention_heads": 12,
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+ "num_hidden_layers": 6,
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  "pad_token_id": 0,
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  "pooler_dropout": 0,
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  "pooler_hidden_act": "gelu",
eval_result_ner.json CHANGED
@@ -1 +1 @@
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- {"ceb_gja": {"precision": 0.7818181818181819, "recall": 0.8775510204081632, "f1": 0.8269230769230769, "accuracy": 0.9868725868725868}, "en_pud": {"precision": 0.8199419167473379, "recall": 0.7879069767441861, "f1": 0.8036053130929791, "accuracy": 0.9821496033245183}, "de_pud": {"precision": 0.8028169014084507, "recall": 0.7680461982675649, "f1": 0.7850467289719626, "accuracy": 0.9771693779007079}, "pt_pud": {"precision": 0.8466019417475729, "recall": 0.7934485896269335, "f1": 0.8191639267261626, "accuracy": 0.9810313154184646}, "ru_pud": {"precision": 0.693437806072478, "recall": 0.6833976833976834, "f1": 0.6883811375789985, "accuracy": 0.9667269439421338}, "sv_pud": {"precision": 0.8518896833503575, "recall": 0.8104956268221575, "f1": 0.8306772908366535, "accuracy": 0.981862025581883}, "tl_trg": {"precision": 0.76, "recall": 0.8260869565217391, "f1": 0.7916666666666667, "accuracy": 0.9877384196185286}, "tl_ugnayan": {"precision": 0.6428571428571429, "recall": 0.8181818181818182, "f1": 0.7200000000000001, "accuracy": 0.9772105742935278}, "zh_gsd": {"precision": 0.6801152737752162, "recall": 0.6153846153846154, "f1": 0.646132785763176, "accuracy": 0.9503829503829504}, "zh_gsdsimp": {"precision": 0.6727272727272727, "recall": 0.6304062909567497, "f1": 0.6508795669824087, "accuracy": 0.9508824508824509}, "hr_set": {"precision": 0.8332028191072827, "recall": 0.7583749109052031, "f1": 0.7940298507462686, "accuracy": 0.9739076669414675}, "da_ddt": {"precision": 0.808, "recall": 0.6778523489932886, "f1": 0.7372262773722629, "accuracy": 0.9792477302204928}, "en_ewt": {"precision": 0.8561840843720039, "recall": 0.8207720588235294, "f1": 0.8381041764429845, "accuracy": 0.9825477148663186}, "pt_bosque": {"precision": 0.8219584569732937, "recall": 0.6839506172839506, "f1": 0.7466307277628033, "accuracy": 0.9729749311693957}, "sr_set": {"precision": 0.8600508905852418, "recall": 0.7981109799291618, "f1": 0.8279240661359463, "accuracy": 0.9718938796952982}, "sk_snk": {"precision": 0.7631578947368421, "recall": 0.6655737704918033, "f1": 0.7110332749562173, "accuracy": 0.9608982412060302}, "sv_talbanken": {"precision": 0.88, "recall": 0.8979591836734694, "f1": 0.888888888888889, "accuracy": 0.9976934779408156}}
 
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