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_full66
+  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_full66
+
+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: 0.4655
+- Precision: 0.8114
+- Recall: 0.8137
+- F1: 0.8126
+- Accuracy: 0.9807
+
+## 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: 66
+- 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 |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.497         | 0.2911 | 500   | 0.9072          | 0.6658    | 0.6611 | 0.6634 | 0.9685   |
+| 0.7828        | 0.5822 | 1000  | 0.7395          | 0.7001    | 0.7482 | 0.7233 | 0.9729   |
+| 0.6597        | 0.8732 | 1500  | 0.6711          | 0.7843    | 0.7178 | 0.7496 | 0.9753   |
+| 0.573         | 1.1643 | 2000  | 0.6213          | 0.7522    | 0.7820 | 0.7668 | 0.9773   |
+| 0.5148        | 1.4554 | 2500  | 0.6290          | 0.7325    | 0.7919 | 0.7610 | 0.9759   |
+| 0.497         | 1.7465 | 3000  | 0.5801          | 0.7759    | 0.7780 | 0.7769 | 0.9778   |
+| 0.4659        | 2.0375 | 3500  | 0.5765          | 0.7926    | 0.7764 | 0.7844 | 0.9786   |
+| 0.4098        | 2.3286 | 4000  | 0.5585          | 0.7868    | 0.7853 | 0.7860 | 0.9789   |
+| 0.4085        | 2.6197 | 4500  | 0.5536          | 0.7862    | 0.8042 | 0.7951 | 0.9793   |
+| 0.3979        | 2.9108 | 5000  | 0.5326          | 0.7902    | 0.8077 | 0.7989 | 0.9798   |
+| 0.3568        | 3.2019 | 5500  | 0.5366          | 0.7925    | 0.7922 | 0.7924 | 0.9793   |
+| 0.3523        | 3.4929 | 6000  | 0.5277          | 0.8058    | 0.7870 | 0.7963 | 0.9792   |
+| 0.3361        | 3.7840 | 6500  | 0.5239          | 0.7851    | 0.8159 | 0.8002 | 0.9792   |
+| 0.3298        | 4.0751 | 7000  | 0.5126          | 0.7993    | 0.8074 | 0.8033 | 0.9800   |
+| 0.3053        | 4.3662 | 7500  | 0.5124          | 0.8074    | 0.7961 | 0.8017 | 0.9796   |
+| 0.3099        | 4.6573 | 8000  | 0.5019          | 0.7953    | 0.8145 | 0.8048 | 0.9799   |
+| 0.3031        | 4.9483 | 8500  | 0.4978          | 0.8133    | 0.8009 | 0.8071 | 0.9801   |
+| 0.2834        | 5.2394 | 9000  | 0.5067          | 0.8160    | 0.8044 | 0.8101 | 0.9804   |
+| 0.2767        | 5.5305 | 9500  | 0.4905          | 0.8104    | 0.8096 | 0.8100 | 0.9804   |
+| 0.2799        | 5.8216 | 10000 | 0.4812          | 0.8092    | 0.8058 | 0.8075 | 0.9804   |
+| 0.2735        | 6.1126 | 10500 | 0.4849          | 0.8110    | 0.8104 | 0.8107 | 0.9805   |
+| 0.261         | 6.4037 | 11000 | 0.4817          | 0.8100    | 0.8114 | 0.8107 | 0.9803   |
+| 0.2587        | 6.6948 | 11500 | 0.4814          | 0.8127    | 0.8152 | 0.8139 | 0.9810   |
+| 0.2593        | 6.9859 | 12000 | 0.4812          | 0.8171    | 0.8090 | 0.8130 | 0.9806   |
+| 0.247         | 7.2770 | 12500 | 0.4816          | 0.8037    | 0.8173 | 0.8104 | 0.9807   |
+| 0.2452        | 7.5680 | 13000 | 0.4688          | 0.8130    | 0.8117 | 0.8124 | 0.9805   |
+| 0.2426        | 7.8591 | 13500 | 0.4700          | 0.8130    | 0.8104 | 0.8117 | 0.9806   |
+| 0.2404        | 8.1502 | 14000 | 0.4680          | 0.8127    | 0.8175 | 0.8151 | 0.9809   |
+| 0.2347        | 8.4413 | 14500 | 0.4723          | 0.8156    | 0.8160 | 0.8158 | 0.9810   |
+| 0.2356        | 8.7324 | 15000 | 0.4720          | 0.8115    | 0.8186 | 0.8151 | 0.9807   |
+| 0.2347        | 9.0234 | 15500 | 0.4634          | 0.8199    | 0.8178 | 0.8188 | 0.9813   |
+| 0.2301        | 9.3145 | 16000 | 0.4631          | 0.8172    | 0.8158 | 0.8165 | 0.9809   |
+| 0.2287        | 9.6056 | 16500 | 0.4621          | 0.8125    | 0.8147 | 0.8136 | 0.9808   |
+| 0.2253        | 9.8967 | 17000 | 0.4655          | 0.8114    | 0.8137 | 0.8126 | 0.9807   |
+
+
+### 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": 0.4594594594594595, "recall": 0.6938775510204082, "f1": 0.5528455284552845, "accuracy": 0.9583011583011583}, "en_pud": {"precision": 0.7752918287937743, "recall": 0.7413953488372093, "f1": 0.7579648121730861, "accuracy": 0.9774744994333208}, "de_pud": {"precision": 0.7380281690140845, "recall": 0.7564966313763234, "f1": 0.747148288973384, "accuracy": 0.9729501664244526}, "pt_pud": {"precision": 0.8408488063660478, "recall": 0.8653321201091901, "f1": 0.852914798206278, "accuracy": 0.9856880420387064}, "ru_pud": {"precision": 0.7012369172216937, "recall": 0.7113899613899614, "f1": 0.7062769525634883, "accuracy": 0.9706535778868509}, "sv_pud": {"precision": 0.8356299212598425, "recall": 0.8250728862973761, "f1": 0.8303178484107578, "accuracy": 0.9836443698888656}, "tl_trg": {"precision": 0.8333333333333334, "recall": 0.8695652173913043, "f1": 0.851063829787234, "accuracy": 0.9904632152588556}, "tl_ugnayan": {"precision": 0.4634146341463415, "recall": 0.5757575757575758, "f1": 0.5135135135135135, "accuracy": 0.968094804010939}, "zh_gsd": {"precision": 0.8099606815203145, "recall": 0.8057366362451108, "f1": 0.8078431372549019, "accuracy": 0.9725274725274725}, "zh_gsdsimp": {"precision": 0.8225165562913908, "recall": 0.8138925294888598, "f1": 0.8181818181818183, "accuracy": 0.9741924741924742}, "hr_set": {"precision": 0.8877266387726639, "recall": 0.9073414112615823, "f1": 0.8974268593584772, "accuracy": 0.9866859027205276}, "da_ddt": {"precision": 0.8605200945626478, "recall": 0.814317673378076, "f1": 0.8367816091954022, "accuracy": 0.9877282250823107}, "en_ewt": {"precision": 0.7806072477962782, "recall": 0.7325367647058824, "f1": 0.7558084400189664, "accuracy": 0.9747778618958441}, "pt_bosque": {"precision": 0.8312858312858313, "recall": 0.8353909465020576, "f1": 0.8333333333333334, "accuracy": 0.9842414142877843}, "sr_set": {"precision": 0.9259259259259259, "recall": 0.9149940968122786, "f1": 0.9204275534441805, "accuracy": 0.9871289729445758}, "sk_snk": {"precision": 0.7758620689655172, "recall": 0.7377049180327869, "f1": 0.7563025210084033, "accuracy": 0.9665515075376885}, "sv_talbanken": {"precision": 0.8349056603773585, "recall": 0.9030612244897959, "f1": 0.8676470588235294, "accuracy": 0.9974481032536684}}

model.safetensors

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training_args.bin

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