Initial Commit

README.md

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+---
+library_name: transformers
+license: mit
+base_model: haryoaw/scenario-TCR-NER_data-univner_half
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: scenario-non-kd-po-ner-full-xlmr_data-univner_half55
+  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. -->
+
+# scenario-non-kd-po-ner-full-xlmr_data-univner_half55
+
+This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1450
+- Precision: 0.8516
+- Recall: 0.8357
+- F1: 0.8436
+- Accuracy: 0.9832
+
+## 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: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 55
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0106        | 0.5828  | 500   | 0.0804          | 0.8385    | 0.8489 | 0.8437 | 0.9838   |
+| 0.0101        | 1.1655  | 1000  | 0.0863          | 0.8549    | 0.8432 | 0.8490 | 0.9838   |
+| 0.009         | 1.7483  | 1500  | 0.0960          | 0.8282    | 0.8557 | 0.8418 | 0.9829   |
+| 0.0077        | 2.3310  | 2000  | 0.1040          | 0.8320    | 0.8498 | 0.8408 | 0.9827   |
+| 0.0077        | 2.9138  | 2500  | 0.0931          | 0.8461    | 0.8478 | 0.8469 | 0.9835   |
+| 0.0054        | 3.4965  | 3000  | 0.0982          | 0.8482    | 0.8523 | 0.8502 | 0.9843   |
+| 0.0061        | 4.0793  | 3500  | 0.1075          | 0.8450    | 0.8355 | 0.8402 | 0.9832   |
+| 0.0053        | 4.6620  | 4000  | 0.1068          | 0.8409    | 0.8536 | 0.8472 | 0.9839   |
+| 0.0047        | 5.2448  | 4500  | 0.1073          | 0.8372    | 0.8592 | 0.8480 | 0.9837   |
+| 0.0047        | 5.8275  | 5000  | 0.1100          | 0.8451    | 0.8585 | 0.8517 | 0.9839   |
+| 0.0034        | 6.4103  | 5500  | 0.1167          | 0.8350    | 0.8497 | 0.8422 | 0.9832   |
+| 0.0034        | 6.9930  | 6000  | 0.1122          | 0.8405    | 0.8478 | 0.8441 | 0.9836   |
+| 0.0035        | 7.5758  | 6500  | 0.1125          | 0.8424    | 0.8419 | 0.8421 | 0.9834   |
+| 0.0032        | 8.1585  | 7000  | 0.1145          | 0.8454    | 0.8504 | 0.8479 | 0.9836   |
+| 0.0035        | 8.7413  | 7500  | 0.1075          | 0.8499    | 0.8407 | 0.8453 | 0.9838   |
+| 0.0027        | 9.3240  | 8000  | 0.1213          | 0.8493    | 0.8384 | 0.8438 | 0.9837   |
+| 0.0031        | 9.9068  | 8500  | 0.1083          | 0.8551    | 0.8440 | 0.8495 | 0.9842   |
+| 0.0027        | 10.4895 | 9000  | 0.1273          | 0.8329    | 0.8639 | 0.8482 | 0.9835   |
+| 0.0024        | 11.0723 | 9500  | 0.1247          | 0.8478    | 0.8411 | 0.8444 | 0.9834   |
+| 0.0021        | 11.6550 | 10000 | 0.1161          | 0.8487    | 0.8378 | 0.8432 | 0.9838   |
+| 0.0019        | 12.2378 | 10500 | 0.1284          | 0.8316    | 0.8556 | 0.8434 | 0.9830   |
+| 0.0021        | 12.8205 | 11000 | 0.1208          | 0.8492    | 0.8510 | 0.8501 | 0.9840   |
+| 0.0015        | 13.4033 | 11500 | 0.1266          | 0.8374    | 0.8499 | 0.8436 | 0.9830   |
+| 0.002         | 13.9860 | 12000 | 0.1236          | 0.8403    | 0.8530 | 0.8466 | 0.9832   |
+| 0.0016        | 14.5688 | 12500 | 0.1313          | 0.8453    | 0.8409 | 0.8430 | 0.9833   |
+| 0.0013        | 15.1515 | 13000 | 0.1362          | 0.8460    | 0.8482 | 0.8471 | 0.9835   |
+| 0.0015        | 15.7343 | 13500 | 0.1246          | 0.8480    | 0.8511 | 0.8496 | 0.9840   |
+| 0.0012        | 16.3170 | 14000 | 0.1335          | 0.8549    | 0.8423 | 0.8485 | 0.9837   |
+| 0.0014        | 16.8998 | 14500 | 0.1265          | 0.8445    | 0.8433 | 0.8439 | 0.9833   |
+| 0.0009        | 17.4825 | 15000 | 0.1450          | 0.8516    | 0.8357 | 0.8436 | 0.9832   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.14.5
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "haryoaw/scenario-TCR-NER_data-univner_half",
+  "architectures": [
+    "XLMRobertaForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

eval_result_ner.json

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+{"ceb_gja": {"precision": 0.5542168674698795, "recall": 0.9387755102040817, "f1": 0.6969696969696971, "accuracy": 0.9683397683397683}, "en_pud": {"precision": 0.8, "recall": 0.8037209302325582, "f1": 0.8018561484918794, "accuracy": 0.9800245561012467}, "de_pud": {"precision": 0.7734303912647862, "recall": 0.8180943214629451, "f1": 0.7951356407857811, "accuracy": 0.9769818573906521}, "pt_pud": {"precision": 0.8683974932855864, "recall": 0.8826205641492265, "f1": 0.875451263537906, "accuracy": 0.9871405989661213}, "ru_pud": {"precision": 0.7193140794223827, "recall": 0.7693050193050193, "f1": 0.7434701492537313, "accuracy": 0.9733918884009299}, "sv_pud": {"precision": 0.8602661596958175, "recall": 0.8794946550048591, "f1": 0.8697741470446901, "accuracy": 0.9863703082407214}, "tl_trg": {"precision": 0.9166666666666666, "recall": 0.9565217391304348, "f1": 0.9361702127659574, "accuracy": 0.9959128065395095}, "tl_ugnayan": {"precision": 0.6923076923076923, "recall": 0.8181818181818182, "f1": 0.7500000000000001, "accuracy": 0.9808568824065633}, "zh_gsd": {"precision": 0.8696219035202086, "recall": 0.8696219035202086, "f1": 0.8696219035202086, "accuracy": 0.9805194805194806}, "zh_gsdsimp": {"precision": 0.8620236530880421, "recall": 0.8597640891218873, "f1": 0.8608923884514434, "accuracy": 0.97993672993673}, "hr_set": {"precision": 0.9109730848861284, "recall": 0.9408410548823949, "f1": 0.9256661991584852, "accuracy": 0.9897774113767519}, "da_ddt": {"precision": 0.8486238532110092, "recall": 0.8277404921700223, "f1": 0.8380520951302378, "accuracy": 0.9881273071934551}, "en_ewt": {"precision": 0.8045574057843996, "recall": 0.84375, "f1": 0.8236877523553163, "accuracy": 0.9815914252699526}, "pt_bosque": {"precision": 0.878657487091222, "recall": 0.8403292181069959, "f1": 0.8590660496424064, "accuracy": 0.986306332415592}, "sr_set": {"precision": 0.9523241954707986, "recall": 0.9433293978748524, "f1": 0.9478054567022538, "accuracy": 0.9898432711671482}, "sk_snk": {"precision": 0.7984749455337691, "recall": 0.8010928961748633, "f1": 0.7997817785051827, "accuracy": 0.9707914572864321}, "sv_talbanken": {"precision": 0.8497652582159625, "recall": 0.923469387755102, "f1": 0.8850855745721271, "accuracy": 0.9977425528782451}}

model.safetensors

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training_args.bin

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