End of training

README.md

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+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: uniBERT.distilBERT.1
+  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. -->
+
+# uniBERT.distilBERT.1
+
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5970
+- Accuracy: (0.48257372654155495,)
+- F1: (0.48821617755360286,)
+- Precision: (0.5906810375519806,)
+- Recall: 0.4826
+
+## 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: 64
+- 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 | Accuracy               | F1                     | Precision              | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:------:|
+| 3.191         | 1.0   | 163  | 2.8674          | (0.13136729222520108,) | (0.10551643479608444,) | (0.13934178819155285,) | 0.1314 |
+| 2.4168        | 2.0   | 326  | 2.2479          | (0.23771224307417338,) | (0.2196759312115438,)  | (0.46668150798486385,) | 0.2377 |
+| 1.8497        | 3.0   | 489  | 1.9548          | (0.30473637176050045,) | (0.30606381370679575,) | (0.5287854015073502,)  | 0.3047 |
+| 1.2962        | 4.0   | 652  | 1.7795          | (0.36371760500446826,) | (0.3751096263138952,)  | (0.5125374899925749,)  | 0.3637 |
+| 1.176         | 5.0   | 815  | 1.7043          | (0.4066130473637176,)  | (0.41869044107007675,) | (0.5244984950622734,)  | 0.4066 |
+| 0.8751        | 6.0   | 978  | 1.6665          | (0.4316353887399464,)  | (0.44145109290311435,) | (0.532683425541351,)   | 0.4316 |
+| 0.7541        | 7.0   | 1141 | 1.6273          | (0.4450402144772118,)  | (0.4544413917407461,)  | (0.5812233728930422,)  | 0.4450 |
+| 0.6257        | 8.0   | 1304 | 1.6054          | (0.46291331546023234,) | (0.46971058022945406,) | (0.5769805019581083,)  | 0.4629 |
+| 0.5855        | 9.0   | 1467 | 1.5948          | (0.47542448614834676,) | (0.4826847668965276,)  | (0.5865283417376732,)  | 0.4754 |
+| 0.5672        | 10.0  | 1630 | 1.5970          | (0.48257372654155495,) | (0.48821617755360286,) | (0.5906810375519806,)  | 0.4826 |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

@@ -0,0 +1,146 @@
+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-1",
+    "2": "I-1",
+    "3": "B-2",
+    "4": "I-2",
+    "5": "B-3",
+    "6": "I-3",
+    "7": "B-4",
+    "8": "I-4",
+    "9": "B-5",
+    "10": "I-5",
+    "11": "B-6",
+    "12": "I-6",
+    "13": "B-7",
+    "14": "I-7",
+    "15": "B-8",
+    "16": "I-8",
+    "17": "B-9",
+    "18": "I-9",
+    "19": "B-10",
+    "20": "I-10",
+    "21": "B-11",
+    "22": "I-11",
+    "23": "B-12",
+    "24": "I-12",
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+    "26": "I-13",
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+    "28": "I-14",
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+    "30": "I-15",
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+    "54": "I-27",
+    "55": "B-28",
+    "56": "I-28",
+    "57": "B-29",
+    "58": "I-29"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
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+    "B-11": 21,
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+    "I-8": 16,
+    "I-9": 18,
+    "O": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:d4be0e5c5187e28c14d19ead6c9f70f70b33e4f49ddee64a32387b9db7f761a7
+size 265645356

special_tokens_map.json

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

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "103": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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