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-non-kd-scr-ner-full_data-univner_full55
+  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-scr-ner-full_data-univner_full55
+
+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: 0.1084
+- Precision: 0.8506
+- Recall: 0.8628
+- F1: 0.8567
+- Accuracy: 0.9845
+
+## 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 |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0097        | 0.2910 | 500   | 0.0794          | 0.8535    | 0.8668 | 0.8601 | 0.9850   |
+| 0.0108        | 0.5821 | 1000  | 0.0802          | 0.8457    | 0.8634 | 0.8544 | 0.9846   |
+| 0.0115        | 0.8731 | 1500  | 0.0724          | 0.8486    | 0.8693 | 0.8588 | 0.9850   |
+| 0.0087        | 1.1641 | 2000  | 0.0877          | 0.8448    | 0.8680 | 0.8562 | 0.9841   |
+| 0.008         | 1.4552 | 2500  | 0.0810          | 0.8576    | 0.8645 | 0.8610 | 0.9850   |
+| 0.0084        | 1.7462 | 3000  | 0.0844          | 0.8508    | 0.8661 | 0.8584 | 0.9846   |
+| 0.0081        | 2.0373 | 3500  | 0.0859          | 0.8524    | 0.8634 | 0.8579 | 0.9846   |
+| 0.0066        | 2.3283 | 4000  | 0.0920          | 0.8386    | 0.8778 | 0.8577 | 0.9845   |
+| 0.0065        | 2.6193 | 4500  | 0.0907          | 0.8480    | 0.8624 | 0.8551 | 0.9841   |
+| 0.0074        | 2.9104 | 5000  | 0.0873          | 0.8342    | 0.8665 | 0.8500 | 0.9838   |
+| 0.0056        | 3.2014 | 5500  | 0.0997          | 0.8490    | 0.8602 | 0.8546 | 0.9842   |
+| 0.0056        | 3.4924 | 6000  | 0.0908          | 0.8451    | 0.8696 | 0.8571 | 0.9847   |
+| 0.0049        | 3.7835 | 6500  | 0.0991          | 0.8534    | 0.8621 | 0.8577 | 0.9849   |
+| 0.0053        | 4.0745 | 7000  | 0.1107          | 0.8444    | 0.8641 | 0.8541 | 0.9844   |
+| 0.0041        | 4.3655 | 7500  | 0.0994          | 0.8414    | 0.8658 | 0.8534 | 0.9839   |
+| 0.0047        | 4.6566 | 8000  | 0.1016          | 0.8498    | 0.8664 | 0.8580 | 0.9839   |
+| 0.0052        | 4.9476 | 8500  | 0.1000          | 0.8369    | 0.8746 | 0.8554 | 0.9844   |
+| 0.0039        | 5.2386 | 9000  | 0.1097          | 0.8292    | 0.8777 | 0.8527 | 0.9835   |
+| 0.0042        | 5.5297 | 9500  | 0.1000          | 0.8451    | 0.8703 | 0.8575 | 0.9844   |
+| 0.0042        | 5.8207 | 10000 | 0.1087          | 0.8468    | 0.8612 | 0.8539 | 0.9841   |
+| 0.0042        | 6.1118 | 10500 | 0.1120          | 0.8312    | 0.8753 | 0.8527 | 0.9839   |
+| 0.0032        | 6.4028 | 11000 | 0.1144          | 0.8510    | 0.8540 | 0.8525 | 0.9842   |
+| 0.0039        | 6.6938 | 11500 | 0.1075          | 0.8330    | 0.8689 | 0.8506 | 0.9837   |
+| 0.0038        | 6.9849 | 12000 | 0.1112          | 0.8295    | 0.8677 | 0.8482 | 0.9833   |
+| 0.0031        | 7.2759 | 12500 | 0.1084          | 0.8506    | 0.8628 | 0.8567 | 0.9845   |
+
+
+### 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": [
+    "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": 0.7580645161290323, "recall": 0.9591836734693877, "f1": 0.8468468468468467, "accuracy": 0.9861003861003861}, "en_pud": {"precision": 0.8141509433962264, "recall": 0.8027906976744186, "f1": 0.8084309133489461, "accuracy": 0.9816301473366075}, "de_pud": {"precision": 0.7988826815642458, "recall": 0.8257940327237728, "f1": 0.8121154756270705, "accuracy": 0.9791383432562937}, "pt_pud": {"precision": 0.8729037952338923, "recall": 0.899909008189263, "f1": 0.8862007168458782, "accuracy": 0.9891485453069595}, "ru_pud": {"precision": 0.7230347349177331, "recall": 0.7635135135135135, "f1": 0.7427230046948357, "accuracy": 0.9740635494704211}, "sv_pud": {"precision": 0.8643067846607669, "recall": 0.8542274052478134, "f1": 0.8592375366568914, "accuracy": 0.9857412455441392}, "tl_trg": {"precision": 1.0, "recall": 0.9565217391304348, "f1": 0.9777777777777777, "accuracy": 0.9986376021798365}, "tl_ugnayan": {"precision": 0.6486486486486487, "recall": 0.7272727272727273, "f1": 0.6857142857142857, "accuracy": 0.9735642661804923}, "zh_gsd": {"precision": 0.8492063492063492, "recall": 0.8370273794002607, "f1": 0.8430728824688116, "accuracy": 0.9784382284382285}, "zh_gsdsimp": {"precision": 0.8494055482166446, "recall": 0.8427260812581914, "f1": 0.8460526315789473, "accuracy": 0.9792707292707292}, "hr_set": {"precision": 0.9159138290479499, "recall": 0.9394155381325731, "f1": 0.9275158339197747, "accuracy": 0.9915086562242375}, "da_ddt": {"precision": 0.8714953271028038, "recall": 0.8344519015659956, "f1": 0.8525714285714285, "accuracy": 0.9888257008879577}, "en_ewt": {"precision": 0.8059149722735675, "recall": 0.8014705882352942, "f1": 0.8036866359447005, "accuracy": 0.9794796190779774}, "pt_bosque": {"precision": 0.8819157720891825, "recall": 0.8790123456790123, "f1": 0.8804616652926629, "accuracy": 0.9875742645993334}, "sr_set": {"precision": 0.9653937947494033, "recall": 0.9551357733175915, "f1": 0.9602373887240355, "accuracy": 0.9926451273969005}, "sk_snk": {"precision": 0.825503355704698, "recall": 0.8065573770491803, "f1": 0.8159203980099503, "accuracy": 0.9719692211055276}, "sv_talbanken": {"precision": 0.8921568627450981, "recall": 0.9285714285714286, "f1": 0.9099999999999999, "accuracy": 0.9983314521273985}}

model.safetensors

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training_args.bin

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