Initial Commit

README.md

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+---
+library_name: transformers
+license: mit
+base_model: haryoaw/scenario-TCR-NER_data-univner_full
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: scenario-kd-scr-ner-full-xlmr_data-univner_full44
+  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-kd-scr-ner-full-xlmr_data-univner_full44
+
+This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3300
+- Precision: 0.4985
+- Recall: 0.4665
+- F1: 0.4819
+- Accuracy: 0.9525
+
+## 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: 8
+- eval_batch_size: 32
+- seed: 44
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 3.061         | 0.2911 | 500   | 2.5055          | 0.3093    | 0.0105 | 0.0204 | 0.9242   |
+| 2.362         | 0.5822 | 1000  | 2.2363          | 0.2161    | 0.1544 | 0.1801 | 0.9254   |
+| 2.1193        | 0.8732 | 1500  | 2.0821          | 0.2713    | 0.1587 | 0.2003 | 0.9303   |
+| 1.9507        | 1.1643 | 2000  | 1.9867          | 0.3366    | 0.1861 | 0.2397 | 0.9334   |
+| 1.8372        | 1.4554 | 2500  | 1.9044          | 0.3094    | 0.2479 | 0.2753 | 0.9344   |
+| 1.7489        | 1.7465 | 3000  | 1.8270          | 0.3812    | 0.2516 | 0.3031 | 0.9379   |
+| 1.6638        | 2.0375 | 3500  | 1.7473          | 0.3617    | 0.2910 | 0.3225 | 0.9391   |
+| 1.5358        | 2.3286 | 4000  | 1.7031          | 0.3968    | 0.3004 | 0.3419 | 0.9404   |
+| 1.5116        | 2.6197 | 4500  | 1.6644          | 0.3930    | 0.3282 | 0.3577 | 0.9414   |
+| 1.4648        | 2.9108 | 5000  | 1.6152          | 0.4069    | 0.3223 | 0.3597 | 0.9428   |
+| 1.3588        | 3.2019 | 5500  | 1.5809          | 0.4327    | 0.3487 | 0.3862 | 0.9440   |
+| 1.322         | 3.4929 | 6000  | 1.5435          | 0.4202    | 0.3956 | 0.4076 | 0.9435   |
+| 1.2743        | 3.7840 | 6500  | 1.5010          | 0.4373    | 0.3922 | 0.4135 | 0.9463   |
+| 1.2353        | 4.0751 | 7000  | 1.5160          | 0.4283    | 0.3868 | 0.4065 | 0.9458   |
+| 1.1701        | 4.3662 | 7500  | 1.4797          | 0.4519    | 0.3956 | 0.4219 | 0.9470   |
+| 1.1448        | 4.6573 | 8000  | 1.4640          | 0.4622    | 0.4053 | 0.4319 | 0.9473   |
+| 1.1258        | 4.9483 | 8500  | 1.4496          | 0.4240    | 0.4115 | 0.4177 | 0.9466   |
+| 1.0504        | 5.2394 | 9000  | 1.4419          | 0.4783    | 0.4160 | 0.4449 | 0.9486   |
+| 1.0311        | 5.5305 | 9500  | 1.4270          | 0.4562    | 0.4122 | 0.4331 | 0.9479   |
+| 1.0299        | 5.8216 | 10000 | 1.3948          | 0.4564    | 0.4294 | 0.4425 | 0.9497   |
+| 1.0016        | 6.1126 | 10500 | 1.4108          | 0.4675    | 0.4333 | 0.4498 | 0.9495   |
+| 0.9546        | 6.4037 | 11000 | 1.3817          | 0.4850    | 0.4418 | 0.4624 | 0.9508   |
+| 0.9384        | 6.6948 | 11500 | 1.3723          | 0.4672    | 0.4425 | 0.4545 | 0.9508   |
+| 0.9259        | 6.9859 | 12000 | 1.3574          | 0.4837    | 0.4398 | 0.4607 | 0.9508   |
+| 0.8941        | 7.2770 | 12500 | 1.3871          | 0.5094    | 0.4393 | 0.4718 | 0.9514   |
+| 0.8827        | 7.5680 | 13000 | 1.3698          | 0.4918    | 0.4262 | 0.4567 | 0.9514   |
+| 0.8776        | 7.8591 | 13500 | 1.3510          | 0.4885    | 0.4467 | 0.4667 | 0.9517   |
+| 0.8552        | 8.1502 | 14000 | 1.3549          | 0.4970    | 0.4530 | 0.4740 | 0.9519   |
+| 0.8392        | 8.4413 | 14500 | 1.3413          | 0.4934    | 0.4565 | 0.4742 | 0.9521   |
+| 0.8463        | 8.7324 | 15000 | 1.3523          | 0.5150    | 0.4409 | 0.4751 | 0.9521   |
+| 0.8301        | 9.0234 | 15500 | 1.3371          | 0.4934    | 0.4701 | 0.4815 | 0.9521   |
+| 0.8104        | 9.3145 | 16000 | 1.3385          | 0.4966    | 0.4667 | 0.4812 | 0.9527   |
+| 0.807         | 9.6056 | 16500 | 1.3347          | 0.4969    | 0.4615 | 0.4786 | 0.9522   |
+| 0.8125        | 9.8967 | 17000 | 1.3300          | 0.4985    | 0.4665 | 0.4819 | 0.9525   |
+
+
+### 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_full",
+  "architectures": [
+    "XLMRobertaForTokenClassificationKD"
+  ],
+  "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": 6,
+  "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": 0.43137254901960786, "recall": 0.4489795918367347, "f1": 0.43999999999999995, "accuracy": 0.9505791505791505}, "en_pud": {"precision": 0.45323741007194246, "recall": 0.3516279069767442, "f1": 0.39601885804085907, "accuracy": 0.9436626369474878}, "de_pud": {"precision": 0.16398467432950192, "recall": 0.20596727622714148, "f1": 0.1825938566552901, "accuracy": 0.8917537855702967}, "pt_pud": {"precision": 0.5004926108374385, "recall": 0.462238398544131, "f1": 0.4806054872280038, "accuracy": 0.9549280129875678}, "ru_pud": {"precision": 0.014035087719298246, "recall": 0.019305019305019305, "f1": 0.016253555465258026, "accuracy": 0.7890984241797985}, "sv_pud": {"precision": 0.5007923930269413, "recall": 0.30709426627793973, "f1": 0.3807228915662651, "accuracy": 0.9433319354162298}, "tl_trg": {"precision": 0.35294117647058826, "recall": 0.2608695652173913, "f1": 0.3, "accuracy": 0.9645776566757494}, "tl_ugnayan": {"precision": 0.0, "recall": 0.0, "f1": 0.0, "accuracy": 0.927073837739289}, "zh_gsd": {"precision": 0.3951219512195122, "recall": 0.31681877444589307, "f1": 0.35166425470332846, "accuracy": 0.9151681651681651}, "zh_gsdsimp": {"precision": 0.40176991150442476, "recall": 0.29750982961992134, "f1": 0.34186746987951805, "accuracy": 0.9177489177489178}, "hr_set": {"precision": 0.673734610123119, "recall": 0.7020669992872416, "f1": 0.68760907504363, "accuracy": 0.9654575432811212}, "da_ddt": {"precision": 0.5538847117794486, "recall": 0.49440715883668906, "f1": 0.5224586288416077, "accuracy": 0.9668761847750175}, "en_ewt": {"precision": 0.5574324324324325, "recall": 0.45496323529411764, "f1": 0.5010121457489878, "accuracy": 0.9565685141650396}, "pt_bosque": {"precision": 0.5150316455696202, "recall": 0.5358024691358024, "f1": 0.5252117789431222, "accuracy": 0.9589552238805971}, "sr_set": {"precision": 0.7125279642058165, "recall": 0.7520661157024794, "f1": 0.7317633543940265, "accuracy": 0.9632256369845023}, "sk_snk": {"precision": 0.35655058043117743, "recall": 0.23497267759562843, "f1": 0.28326745718050067, "accuracy": 0.9133951005025126}, "sv_talbanken": {"precision": 0.6855345911949685, "recall": 0.5561224489795918, "f1": 0.6140845070422535, "accuracy": 0.9936202581341709}}

model.safetensors

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training_args.bin

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