End of training

README.md

@@ -0,0 +1,74 @@
+---
+base_model: allenai/scibert_scivocab_uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: uniBERT.SciBERT.2
+  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.SciBERT.2
+
+This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5863
+- Accuracy: (0.5884718498659517,)
+- F1: (0.5835493983611322,)
+- Precision: (0.5880118425320139,)
+- Recall: 0.5885
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:----------------------:|:---------------------:|:------:|
+| 2.6736        | 1.0   | 187  | 2.1773          | (0.3847184986595174,) | (0.3652816466654799,)  | (0.3985942864108866,) | 0.3847 |
+| 1.6286        | 2.0   | 374  | 1.6625          | (0.4906166219839142,) | (0.48229779243148563,) | (0.5287776487828597,) | 0.4906 |
+| 1.1733        | 3.0   | 561  | 1.5601          | (0.5281501340482574,) | (0.5221085418789655,)  | (0.5430006354909301,) | 0.5282 |
+| 0.8032        | 4.0   | 748  | 1.4738          | (0.5549597855227882,) | (0.5499270608655985,)  | (0.5615902558999348,) | 0.5550 |
+| 0.5888        | 5.0   | 935  | 1.4584          | (0.5603217158176944,) | (0.5559524005998449,)  | (0.5684946987230237,) | 0.5603 |
+| 0.4449        | 6.0   | 1122 | 1.4952          | (0.5764075067024129,) | (0.5740862941630532,)  | (0.5860221500122856,) | 0.5764 |
+| 0.271         | 7.0   | 1309 | 1.5141          | (0.5777479892761395,) | (0.5724486836239684,)  | (0.5756237402682504,) | 0.5777 |
+| 0.2036        | 8.0   | 1496 | 1.5745          | (0.5737265415549598,) | (0.5706283325637723,)  | (0.5784921965802793,) | 0.5737 |
+| 0.1993        | 9.0   | 1683 | 1.5754          | (0.5831099195710456,) | (0.5792457295024093,)  | (0.5837479506310695,) | 0.5831 |
+| 0.1485        | 10.0  | 1870 | 1.5863          | (0.5884718498659517,) | (0.5835493983611322,)  | (0.5880118425320139,) | 0.5885 |
+
+
+### Framework versions
+
+- Transformers 4.39.3
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "allenai/scibert_scivocab_uncased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-1",
+    "2": "I-1",
+    "3": "B-2",
+    "4": "I-2",
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31090
+}

model.safetensors

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+oid sha256:abd346fa33e07a631133a3fffc4cade33f5dcbee50ae1a47d49b2cfd004d75fe
+size 437516316

special_tokens_map.json

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

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "normalized": false,
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+  "do_lower_case": true,
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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