Initial Commit

README.md

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+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: scenario-kd-pre-ner-full-xlmr_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-kd-pre-ner-full-xlmr_data-univner_full55
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4608
+- Precision: 0.8156
+- Recall: 0.8175
+- F1: 0.8165
+- Accuracy: 0.9809
+
+## 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: 55
+- 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 |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.4819        | 0.2911 | 500   | 0.9261          | 0.6821    | 0.6631 | 0.6725 | 0.9684   |
+| 0.7602        | 0.5822 | 1000  | 0.7517          | 0.7360    | 0.7280 | 0.7320 | 0.9744   |
+| 0.6658        | 0.8732 | 1500  | 0.6692          | 0.7309    | 0.7741 | 0.7519 | 0.9759   |
+| 0.5797        | 1.1643 | 2000  | 0.6374          | 0.7530    | 0.7754 | 0.7640 | 0.9771   |
+| 0.5073        | 1.4554 | 2500  | 0.6143          | 0.7486    | 0.7814 | 0.7646 | 0.9768   |
+| 0.4981        | 1.7465 | 3000  | 0.5840          | 0.7764    | 0.7886 | 0.7825 | 0.9782   |
+| 0.4623        | 2.0375 | 3500  | 0.5865          | 0.7928    | 0.7762 | 0.7844 | 0.9785   |
+| 0.4163        | 2.3286 | 4000  | 0.5635          | 0.7767    | 0.7969 | 0.7866 | 0.9786   |
+| 0.3968        | 2.6197 | 4500  | 0.5500          | 0.7826    | 0.7844 | 0.7835 | 0.9782   |
+| 0.3882        | 2.9108 | 5000  | 0.5628          | 0.7999    | 0.7878 | 0.7938 | 0.9792   |
+| 0.3671        | 3.2019 | 5500  | 0.5415          | 0.7851    | 0.8002 | 0.7926 | 0.9790   |
+| 0.3478        | 3.4929 | 6000  | 0.5258          | 0.7988    | 0.8015 | 0.8001 | 0.9800   |
+| 0.3402        | 3.7840 | 6500  | 0.5184          | 0.7948    | 0.8113 | 0.8029 | 0.9801   |
+| 0.3272        | 4.0751 | 7000  | 0.5122          | 0.7873    | 0.8145 | 0.8007 | 0.9796   |
+| 0.3138        | 4.3662 | 7500  | 0.5116          | 0.7927    | 0.7995 | 0.7961 | 0.9796   |
+| 0.3078        | 4.6573 | 8000  | 0.5158          | 0.8017    | 0.7970 | 0.7994 | 0.9793   |
+| 0.3041        | 4.9483 | 8500  | 0.4921          | 0.7932    | 0.8155 | 0.8042 | 0.9799   |
+| 0.2874        | 5.2394 | 9000  | 0.5006          | 0.7984    | 0.8055 | 0.8019 | 0.9799   |
+| 0.2805        | 5.5305 | 9500  | 0.4859          | 0.8075    | 0.8091 | 0.8083 | 0.9802   |
+| 0.2803        | 5.8216 | 10000 | 0.4845          | 0.8046    | 0.8123 | 0.8084 | 0.9809   |
+| 0.2738        | 6.1126 | 10500 | 0.4837          | 0.8033    | 0.8117 | 0.8075 | 0.9805   |
+| 0.2618        | 6.4037 | 11000 | 0.4872          | 0.8089    | 0.8108 | 0.8099 | 0.9809   |
+| 0.2625        | 6.6948 | 11500 | 0.4765          | 0.8049    | 0.8075 | 0.8062 | 0.9803   |
+| 0.2588        | 6.9859 | 12000 | 0.4760          | 0.8104    | 0.8155 | 0.8129 | 0.9810   |
+| 0.2471        | 7.2770 | 12500 | 0.4727          | 0.8030    | 0.8146 | 0.8088 | 0.9807   |
+| 0.2448        | 7.5680 | 13000 | 0.4599          | 0.7999    | 0.8207 | 0.8101 | 0.9808   |
+| 0.2448        | 7.8591 | 13500 | 0.4828          | 0.8114    | 0.8059 | 0.8087 | 0.9803   |
+| 0.2422        | 8.1502 | 14000 | 0.4690          | 0.8170    | 0.8090 | 0.8130 | 0.9809   |
+| 0.2368        | 8.4413 | 14500 | 0.4695          | 0.8105    | 0.8145 | 0.8125 | 0.9810   |
+| 0.2356        | 8.7324 | 15000 | 0.4659          | 0.8076    | 0.8121 | 0.8099 | 0.9806   |
+| 0.2312        | 9.0234 | 15500 | 0.4677          | 0.8194    | 0.8124 | 0.8159 | 0.9810   |
+| 0.2333        | 9.3145 | 16000 | 0.4627          | 0.8115    | 0.8153 | 0.8134 | 0.9810   |
+| 0.2279        | 9.6056 | 16500 | 0.4672          | 0.8111    | 0.8146 | 0.8128 | 0.9807   |
+| 0.2299        | 9.8967 | 17000 | 0.4608          | 0.8156    | 0.8175 | 0.8165 | 0.9809   |
+
+
+### 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": "FacebookAI/xlm-roberta-base",
+  "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": 0.5373134328358209, "recall": 0.7346938775510204, "f1": 0.6206896551724138, "accuracy": 0.9652509652509652}, "en_pud": {"precision": 0.7833173537871524, "recall": 0.76, "f1": 0.7714825306893295, "accuracy": 0.9784661881375142}, "de_pud": {"precision": 0.7214285714285714, "recall": 0.7776708373435997, "f1": 0.7484946734599351, "accuracy": 0.9728564061694248}, "pt_pud": {"precision": 0.8086580086580086, "recall": 0.8498635122838945, "f1": 0.828748890860692, "accuracy": 0.9841927628487205}, "ru_pud": {"precision": 0.6859273066169618, "recall": 0.7104247104247104, "f1": 0.6979611190137506, "accuracy": 0.9695169206923275}, "sv_pud": {"precision": 0.8308457711442786, "recall": 0.8114674441205053, "f1": 0.8210422812192724, "accuracy": 0.9827531977353743}, "tl_trg": {"precision": 0.8181818181818182, "recall": 0.782608695652174, "f1": 0.8, "accuracy": 0.9918256130790191}, "tl_ugnayan": {"precision": 0.5365853658536586, "recall": 0.6666666666666666, "f1": 0.5945945945945946, "accuracy": 0.9699179580674567}, "zh_gsd": {"precision": 0.8273092369477911, "recall": 0.8057366362451108, "f1": 0.8163804491413473, "accuracy": 0.9744422244422244}, "zh_gsdsimp": {"precision": 0.8223684210526315, "recall": 0.8191349934469201, "f1": 0.8207485226526592, "accuracy": 0.9756077256077256}, "hr_set": {"precision": 0.8713892709766162, "recall": 0.9030648610121169, "f1": 0.8869443472173609, "accuracy": 0.986479802143446}, "da_ddt": {"precision": 0.8414918414918415, "recall": 0.8076062639821029, "f1": 0.8242009132420092, "accuracy": 0.9871296019155942}, "en_ewt": {"precision": 0.789980732177264, "recall": 0.7536764705882353, "f1": 0.7714016933207902, "accuracy": 0.9767701318882736}, "pt_bosque": {"precision": 0.8534979423868313, "recall": 0.8534979423868313, "f1": 0.8534979423868313, "accuracy": 0.9857629329082742}, "sr_set": {"precision": 0.9453032104637337, "recall": 0.9386068476977568, "f1": 0.9419431279620853, "accuracy": 0.9908064092461255}, "sk_snk": {"precision": 0.7670454545454546, "recall": 0.7377049180327869, "f1": 0.7520891364902507, "accuracy": 0.9667870603015075}, "sv_talbanken": {"precision": 0.8333333333333334, "recall": 0.8928571428571429, "f1": 0.8620689655172413, "accuracy": 0.997399028316239}}

model.safetensors

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training_args.bin

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