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_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-pre-ner-full-xlmr_data-univner_full44
+
+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: 0.4609
+- Precision: 0.8151
+- Recall: 0.8199
+- F1: 0.8175
+- Accuracy: 0.9812
+
+## 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 |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.4889        | 0.2911 | 500   | 0.8586          | 0.6595    | 0.7179 | 0.6875 | 0.9708   |
+| 0.756         | 0.5822 | 1000  | 0.7200          | 0.7071    | 0.7746 | 0.7393 | 0.9739   |
+| 0.6626        | 0.8732 | 1500  | 0.6671          | 0.7417    | 0.7530 | 0.7473 | 0.9760   |
+| 0.5626        | 1.1643 | 2000  | 0.6205          | 0.7676    | 0.7785 | 0.7730 | 0.9772   |
+| 0.514         | 1.4554 | 2500  | 0.6545          | 0.8094    | 0.7420 | 0.7743 | 0.9774   |
+| 0.4905        | 1.7465 | 3000  | 0.5754          | 0.7770    | 0.7869 | 0.7819 | 0.9782   |
+| 0.461         | 2.0375 | 3500  | 0.5559          | 0.7697    | 0.8101 | 0.7894 | 0.9790   |
+| 0.4097        | 2.3286 | 4000  | 0.5613          | 0.7862    | 0.7836 | 0.7849 | 0.9785   |
+| 0.3973        | 2.6197 | 4500  | 0.5514          | 0.7850    | 0.8003 | 0.7926 | 0.9795   |
+| 0.3878        | 2.9108 | 5000  | 0.5299          | 0.7913    | 0.8039 | 0.7975 | 0.9791   |
+| 0.3579        | 3.2019 | 5500  | 0.5424          | 0.8023    | 0.7852 | 0.7936 | 0.9790   |
+| 0.3434        | 3.4929 | 6000  | 0.5077          | 0.7881    | 0.8085 | 0.7982 | 0.9795   |
+| 0.3362        | 3.7840 | 6500  | 0.5244          | 0.8012    | 0.7943 | 0.7977 | 0.9793   |
+| 0.3243        | 4.0751 | 7000  | 0.5158          | 0.8068    | 0.8108 | 0.8088 | 0.9801   |
+| 0.3134        | 4.3662 | 7500  | 0.5081          | 0.8001    | 0.8137 | 0.8069 | 0.9799   |
+| 0.3027        | 4.6573 | 8000  | 0.4989          | 0.8003    | 0.8169 | 0.8085 | 0.9803   |
+| 0.2977        | 4.9483 | 8500  | 0.4926          | 0.8013    | 0.8121 | 0.8067 | 0.9804   |
+| 0.2822        | 5.2394 | 9000  | 0.4905          | 0.8052    | 0.8081 | 0.8067 | 0.9801   |
+| 0.2773        | 5.5305 | 9500  | 0.4864          | 0.8012    | 0.8049 | 0.8031 | 0.9798   |
+| 0.2803        | 5.8216 | 10000 | 0.4883          | 0.7963    | 0.8090 | 0.8026 | 0.9798   |
+| 0.2717        | 6.1126 | 10500 | 0.4941          | 0.8169    | 0.7909 | 0.8037 | 0.9798   |
+| 0.258         | 6.4037 | 11000 | 0.4842          | 0.8008    | 0.8078 | 0.8043 | 0.9802   |
+| 0.2572        | 6.6948 | 11500 | 0.4760          | 0.8129    | 0.8097 | 0.8113 | 0.9805   |
+| 0.2553        | 6.9859 | 12000 | 0.4742          | 0.8119    | 0.8116 | 0.8117 | 0.9809   |
+| 0.2462        | 7.2770 | 12500 | 0.4791          | 0.8116    | 0.8054 | 0.8085 | 0.9806   |
+| 0.2447        | 7.5680 | 13000 | 0.4750          | 0.8017    | 0.8171 | 0.8093 | 0.9804   |
+| 0.2463        | 7.8591 | 13500 | 0.4657          | 0.8179    | 0.8113 | 0.8146 | 0.9811   |
+| 0.2381        | 8.1502 | 14000 | 0.4677          | 0.8025    | 0.8153 | 0.8088 | 0.9805   |
+| 0.2357        | 8.4413 | 14500 | 0.4658          | 0.8135    | 0.8184 | 0.8159 | 0.9810   |
+| 0.2333        | 8.7324 | 15000 | 0.4638          | 0.8144    | 0.8116 | 0.8130 | 0.9807   |
+| 0.234         | 9.0234 | 15500 | 0.4605          | 0.8126    | 0.8165 | 0.8145 | 0.9810   |
+| 0.2297        | 9.3145 | 16000 | 0.4670          | 0.8116    | 0.8080 | 0.8098 | 0.9808   |
+| 0.2258        | 9.6056 | 16500 | 0.4651          | 0.8095    | 0.8142 | 0.8118 | 0.9808   |
+| 0.2272        | 9.8967 | 17000 | 0.4609          | 0.8151    | 0.8199 | 0.8175 | 0.9812   |
+
+
+### 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": 0.5753424657534246, "recall": 0.8571428571428571, "f1": 0.6885245901639344, "accuracy": 0.9698841698841699}, "en_pud": {"precision": 0.7919921875, "recall": 0.7544186046511628, "f1": 0.7727489280609815, "accuracy": 0.978796751038912}, "de_pud": {"precision": 0.7275280898876404, "recall": 0.7478344562078922, "f1": 0.7375415282392026, "accuracy": 0.9727626459143969}, "pt_pud": {"precision": 0.8328888888888889, "recall": 0.8525932666060054, "f1": 0.8426258992805756, "accuracy": 0.9847054299995728}, "ru_pud": {"precision": 0.690566037735849, "recall": 0.7065637065637066, "f1": 0.6984732824427481, "accuracy": 0.9701369155257039}, "sv_pud": {"precision": 0.825098814229249, "recall": 0.8114674441205053, "f1": 0.8182263596276335, "accuracy": 0.982543510169847}, "tl_trg": {"precision": 0.8333333333333334, "recall": 0.8695652173913043, "f1": 0.851063829787234, "accuracy": 0.9904632152588556}, "tl_ugnayan": {"precision": 0.5384615384615384, "recall": 0.6363636363636364, "f1": 0.5833333333333334, "accuracy": 0.9690063810391978}, "zh_gsd": {"precision": 0.817470664928292, "recall": 0.817470664928292, "f1": 0.817470664928292, "accuracy": 0.974025974025974}, "zh_gsdsimp": {"precision": 0.8111979166666666, "recall": 0.8165137614678899, "f1": 0.813847158719791, "accuracy": 0.9731934731934732}, "hr_set": {"precision": 0.8870175438596491, "recall": 0.9009265858873842, "f1": 0.8939179632248939, "accuracy": 0.9877164056059357}, "da_ddt": {"precision": 0.8551068883610451, "recall": 0.8053691275167785, "f1": 0.8294930875576036, "accuracy": 0.986930060860022}, "en_ewt": {"precision": 0.804496578690127, "recall": 0.7564338235294118, "f1": 0.7797252486972998, "accuracy": 0.977208431286608}, "pt_bosque": {"precision": 0.8516075845012366, "recall": 0.8502057613168724, "f1": 0.8509060955518946, "accuracy": 0.9855455731053471}, "sr_set": {"precision": 0.9245283018867925, "recall": 0.9256198347107438, "f1": 0.9250737463126844, "accuracy": 0.9887925750809912}, "sk_snk": {"precision": 0.7808988764044944, "recall": 0.7595628415300546, "f1": 0.7700831024930748, "accuracy": 0.9675722361809045}, "sv_talbanken": {"precision": 0.8373205741626795, "recall": 0.8928571428571429, "f1": 0.8641975308641975, "accuracy": 0.9973499533788095}}

model.safetensors

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training_args.bin

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