Sentiment analysis for stock news

README.md

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+---
+library_name: transformers
+base_model: airesearch/wangchanberta-base-att-spm-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: results
+  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. -->
+
+# results
+
+This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3038
+- Accuracy: 0.9587
+- Precision: 0.9594
+- Recall: 0.9587
+- F1: 0.9589
+
+## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.5153        | 1.0   | 1154  | 0.5267          | 0.7811   | 0.8214    | 0.7811 | 0.7825 |
+| 0.2142        | 2.0   | 2308  | 0.2754          | 0.9223   | 0.9285    | 0.9223 | 0.9233 |
+| 0.1629        | 3.0   | 3462  | 0.3064          | 0.9242   | 0.9337    | 0.9242 | 0.9258 |
+| 0.0114        | 4.0   | 4616  | 0.2063          | 0.9546   | 0.9562    | 0.9546 | 0.9547 |
+| 0.0168        | 5.0   | 5770  | 0.2266          | 0.9521   | 0.9540    | 0.9521 | 0.9523 |
+| 0.0166        | 6.0   | 6924  | 0.2503          | 0.9543   | 0.9556    | 0.9543 | 0.9544 |
+| 0.0716        | 7.0   | 8078  | 0.2382          | 0.9607   | 0.9618    | 0.9607 | 0.9609 |
+| 0.0006        | 8.0   | 9232  | 0.2849          | 0.9536   | 0.9551    | 0.9536 | 0.9538 |
+| 0.0001        | 9.0   | 10386 | 0.2501          | 0.9641   | 0.9649    | 0.9641 | 0.9642 |
+| 0.0005        | 10.0  | 11540 | 0.2606          | 0.9610   | 0.9619    | 0.9610 | 0.9611 |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1

config.json

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+{
+  "architectures": [
+    "CamembertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Positive",
+    "1": "Neutral",
+    "2": "Negative"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Negative": 2,
+    "Neutral": 1,
+    "Positive": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "camembert",
+  "num_attention_head": 12,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 25005
+}

model.safetensors

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sentencepiece.bpe.model

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special_tokens_map.json

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+{
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+  "sep_token": "</s>",
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+}

tokenizer_config.json

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+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "CamembertTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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