Finetuned Erlangshen on custom sentiment dataset

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: '20251023'
+  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. -->
+
+# 20251023
+
+This model is a fine-tuned version of [IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment](https://huggingface.co/IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0056
+- Accuracy: 1.0
+- F1: 1.0
+
+## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1  |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
+| No log        | 1.0   | 50   | 0.0161          | 1.0      | 1.0 |
+| No log        | 2.0   | 100  | 0.0066          | 1.0      | 1.0 |
+| No log        | 3.0   | 150  | 0.0056          | 1.0      | 1.0 |
+
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.2.0
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "dtype": "float32",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "\u4f0f\u7279\u52a0\u7c7b",
+    "1": "\u5a01\u58eb\u5fcc\u7c7b",
+    "2": "\u6717\u59c6\u9152\u7c7b",
+    "3": "\u91d1\u9152\u7c7b",
+    "4": "\u9f99\u820c\u5170\u7c7b"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "\u4f0f\u7279\u52a0\u7c7b": 0,
+    "\u5a01\u58eb\u5fcc\u7c7b": 1,
+    "\u6717\u59c6\u9152\u7c7b": 2,
+    "\u91d1\u9152\u7c7b": 3,
+    "\u9f99\u820c\u5170\u7c7b": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

model.safetensors

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runs/Oct23_08-00-55_142ffdf204a4/events.out.tfevents.1761206464.142ffdf204a4.5503.0

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special_tokens_map.json

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+  "sep_token": "[SEP]",
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+}

tokenizer_config.json

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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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