Initial Commit

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/mdeberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: scenario-kd-pre-ner-full-mdeberta_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-mdeberta_data-univner_full44
+
+This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2650
+- Precision: 0.8107
+- Recall: 0.8117
+- F1: 0.8112
+- Accuracy: 0.9806
+
+## 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 |
+|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.3559        | 0.2911 | 500   | 0.7891          | 0.4246    | 0.3525 | 0.3852 | 0.9433   |
+| 0.7017        | 0.5822 | 1000  | 0.5422          | 0.6316    | 0.6298 | 0.6307 | 0.9646   |
+| 0.528         | 0.8732 | 1500  | 0.4693          | 0.6856    | 0.6830 | 0.6843 | 0.9692   |
+| 0.4354        | 1.1643 | 2000  | 0.4211          | 0.7101    | 0.7376 | 0.7236 | 0.9724   |
+| 0.385         | 1.4554 | 2500  | 0.3893          | 0.7482    | 0.7374 | 0.7428 | 0.9747   |
+| 0.3575        | 1.7465 | 3000  | 0.3713          | 0.7678    | 0.7331 | 0.7500 | 0.9752   |
+| 0.3298        | 2.0375 | 3500  | 0.3550          | 0.7497    | 0.7800 | 0.7645 | 0.9761   |
+| 0.2879        | 2.3286 | 4000  | 0.3492          | 0.7964    | 0.7367 | 0.7654 | 0.9763   |
+| 0.2748        | 2.6197 | 4500  | 0.3272          | 0.7660    | 0.7924 | 0.7790 | 0.9782   |
+| 0.2644        | 2.9108 | 5000  | 0.3192          | 0.7817    | 0.7811 | 0.7814 | 0.9779   |
+| 0.2416        | 3.2019 | 5500  | 0.3239          | 0.8004    | 0.7681 | 0.7839 | 0.9782   |
+| 0.2303        | 3.4929 | 6000  | 0.3085          | 0.7846    | 0.7966 | 0.7905 | 0.9787   |
+| 0.2252        | 3.7840 | 6500  | 0.3051          | 0.7973    | 0.7883 | 0.7928 | 0.9787   |
+| 0.2159        | 4.0751 | 7000  | 0.3045          | 0.7987    | 0.7908 | 0.7948 | 0.9790   |
+| 0.2067        | 4.3662 | 7500  | 0.2979          | 0.7969    | 0.7943 | 0.7956 | 0.9793   |
+| 0.2028        | 4.6573 | 8000  | 0.2924          | 0.7855    | 0.8132 | 0.7991 | 0.9792   |
+| 0.1985        | 4.9483 | 8500  | 0.2904          | 0.8008    | 0.7986 | 0.7997 | 0.9791   |
+| 0.1867        | 5.2394 | 9000  | 0.2884          | 0.8       | 0.8033 | 0.8017 | 0.9797   |
+| 0.1838        | 5.5305 | 9500  | 0.2841          | 0.7997    | 0.8220 | 0.8107 | 0.9800   |
+| 0.1838        | 5.8216 | 10000 | 0.2810          | 0.7895    | 0.8165 | 0.8028 | 0.9798   |
+| 0.1786        | 6.1126 | 10500 | 0.2767          | 0.8065    | 0.8150 | 0.8108 | 0.9802   |
+| 0.1719        | 6.4037 | 11000 | 0.2790          | 0.8133    | 0.8057 | 0.8095 | 0.9803   |
+| 0.1706        | 6.6948 | 11500 | 0.2795          | 0.8140    | 0.7983 | 0.8061 | 0.9802   |
+| 0.1695        | 6.9859 | 12000 | 0.2723          | 0.8124    | 0.8121 | 0.8123 | 0.9807   |
+| 0.1638        | 7.2770 | 12500 | 0.2726          | 0.8070    | 0.8078 | 0.8074 | 0.9803   |
+| 0.162         | 7.5680 | 13000 | 0.2724          | 0.8118    | 0.8173 | 0.8146 | 0.9807   |
+| 0.1619        | 7.8591 | 13500 | 0.2678          | 0.8018    | 0.8235 | 0.8125 | 0.9805   |
+| 0.1594        | 8.1502 | 14000 | 0.2719          | 0.8103    | 0.8068 | 0.8086 | 0.9800   |
+| 0.1571        | 8.4413 | 14500 | 0.2688          | 0.8097    | 0.8127 | 0.8112 | 0.9805   |
+| 0.1585        | 8.7324 | 15000 | 0.2673          | 0.8126    | 0.8150 | 0.8138 | 0.9806   |
+| 0.1546        | 9.0234 | 15500 | 0.2658          | 0.8105    | 0.8120 | 0.8112 | 0.9805   |
+| 0.1534        | 9.3145 | 16000 | 0.2652          | 0.8101    | 0.8198 | 0.8149 | 0.9807   |
+| 0.1535        | 9.6056 | 16500 | 0.2646          | 0.8097    | 0.8140 | 0.8119 | 0.9807   |
+| 0.1531        | 9.8967 | 17000 | 0.2650          | 0.8107    | 0.8117 | 0.8112 | 0.9806   |
+
+
+### 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": "microsoft/mdeberta-v3-base",
+  "architectures": [
+    "DebertaForTokenClassificationKD"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "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-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 0,
+  "vocab_size": 251000
+}

eval_result_ner.json

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+{"ceb_gja": {"precision": 0.6323529411764706, "recall": 0.8775510204081632, "f1": 0.7350427350427351, "accuracy": 0.976061776061776}, "en_pud": {"precision": 0.7895771878072763, "recall": 0.7469767441860465, "f1": 0.7676864244741873, "accuracy": 0.9780884019644881}, "de_pud": {"precision": 0.7371375116931712, "recall": 0.7584215591915303, "f1": 0.7476280834914611, "accuracy": 0.9724344850217993}, "pt_pud": {"precision": 0.810928013876843, "recall": 0.8507734303912647, "f1": 0.830373001776199, "accuracy": 0.9829538172341608}, "ru_pud": {"precision": 0.6553930530164533, "recall": 0.6920849420849421, "f1": 0.6732394366197183, "accuracy": 0.9688452596228365}, "sv_pud": {"precision": 0.8186323092170465, "recall": 0.8027210884353742, "f1": 0.8105986261040234, "accuracy": 0.9815999161249738}, "tl_trg": {"precision": 0.6785714285714286, "recall": 0.8260869565217391, "f1": 0.7450980392156864, "accuracy": 0.9836512261580381}, "tl_ugnayan": {"precision": 0.5581395348837209, "recall": 0.7272727272727273, "f1": 0.6315789473684211, "accuracy": 0.9690063810391978}, "zh_gsd": {"precision": 0.7762148337595908, "recall": 0.7913950456323338, "f1": 0.7837314396384765, "accuracy": 0.9711122211122211}, "zh_gsdsimp": {"precision": 0.7893368010403121, "recall": 0.7955439056356488, "f1": 0.7924281984334205, "accuracy": 0.9726107226107226}, "hr_set": {"precision": 0.8732782369146006, "recall": 0.9037776193870278, "f1": 0.8882661996497373, "accuracy": 0.9865210222588623}, "da_ddt": {"precision": 0.8513189448441247, "recall": 0.7941834451901566, "f1": 0.8217592592592593, "accuracy": 0.9859323555821611}, "en_ewt": {"precision": 0.7971291866028708, "recall": 0.765625, "f1": 0.7810595405532114, "accuracy": 0.9782842570825199}, "pt_bosque": {"precision": 0.8327922077922078, "recall": 0.8444444444444444, "f1": 0.8385778504290968, "accuracy": 0.9854368932038835}, "sr_set": {"precision": 0.9155920281359906, "recall": 0.922077922077922, "f1": 0.9188235294117646, "accuracy": 0.9883547850450923}, "sk_snk": {"precision": 0.7766203703703703, "recall": 0.7333333333333333, "f1": 0.7543563799887577, "accuracy": 0.9663159547738693}, "sv_talbanken": {"precision": 0.8262910798122066, "recall": 0.8979591836734694, "f1": 0.8606356968215159, "accuracy": 0.9970555037542327}}

model.safetensors

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training_args.bin

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