End of training

README.md

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+---
+license: mit
+base_model: Gladiator/microsoft-deberta-v3-large_ner_conll2003
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: ner_tag_model
+  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. -->
+
+# ner_tag_model
+
+This model is a fine-tuned version of [Gladiator/microsoft-deberta-v3-large_ner_conll2003](https://huggingface.co/Gladiator/microsoft-deberta-v3-large_ner_conll2003) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1712
+- Precision: 0.8569
+- Recall: 0.8551
+- F1: 0.8560
+- Accuracy: 0.9151
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.2322        | 1.0   | 2495  | 0.1925          | 0.7990    | 0.7924 | 0.7957 | 0.8969   |
+| 0.1674        | 2.0   | 4990  | 0.1488          | 0.8218    | 0.8316 | 0.8267 | 0.9116   |
+| 0.1381        | 3.0   | 7485  | 0.1438          | 0.8204    | 0.8350 | 0.8276 | 0.9130   |
+| 0.1284        | 4.0   | 9980  | 0.1381          | 0.8419    | 0.8405 | 0.8412 | 0.9148   |
+| 0.1198        | 5.0   | 12475 | 0.1400          | 0.8280    | 0.8410 | 0.8345 | 0.9148   |
+| 0.1155        | 6.0   | 14970 | 0.1395          | 0.8379    | 0.8467 | 0.8423 | 0.9154   |
+| 0.1125        | 7.0   | 17465 | 0.1496          | 0.8438    | 0.8487 | 0.8462 | 0.9151   |
+| 0.1068        | 8.0   | 19960 | 0.1510          | 0.8518    | 0.8529 | 0.8523 | 0.9156   |
+| 0.1002        | 9.0   | 22455 | 0.1616          | 0.8536    | 0.8539 | 0.8537 | 0.9150   |
+| 0.0964        | 10.0  | 24950 | 0.1712          | 0.8569    | 0.8551 | 0.8560 | 0.9151   |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.14.5
+- Tokenizers 0.13.3

added_tokens.json

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+{
+  "[MASK]": 128000
+}

config.json

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+{
+  "_name_or_path": "Gladiator/microsoft-deberta-v3-large_ner_conll2003",
+  "architectures": [
+    "DebertaV2ForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "ProductNameEn",
+    "1": "TradeMarkEn",
+    "2": "Country",
+    "3": "HSCode",
+    "4": "HSCodeEn",
+    "5": "ManufacturerEn",
+    "6": "ModelNo"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "Country": 2,
+    "HSCode": 3,
+    "HSCodeEn": 4,
+    "ManufacturerEn": 5,
+    "ModelNo": 6,
+    "ProductNameEn": 0,
+    "TradeMarkEn": 1
+  },
+  "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": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.1",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

spm.model

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tokenizer_config.json

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+{
+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "stride": 0,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

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