Uploading crypto fundamental news classifier model

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: crypto_fundamental_news_text_classifier-distilbert-base-uncased
+  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. -->
+
+# crypto_fundamental_news_text_classifier-distilbert-base-uncased
+
+This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3716
+- Accuracy: 0.9194
+
+## 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: 1e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- 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: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.0913        | 1.0   | 8    | 1.1070          | 0.2097   |
+| 1.0507        | 2.0   | 16   | 1.0611          | 0.4032   |
+| 0.9942        | 3.0   | 24   | 0.9997          | 0.5161   |
+| 0.8923        | 4.0   | 32   | 0.9018          | 0.5968   |
+| 0.7789        | 5.0   | 40   | 0.8149          | 0.6774   |
+| 0.675         | 6.0   | 48   | 0.7557          | 0.7903   |
+| 0.6047        | 7.0   | 56   | 0.6935          | 0.7903   |
+| 0.5335        | 8.0   | 64   | 0.6468          | 0.8548   |
+| 0.4758        | 9.0   | 72   | 0.6036          | 0.8871   |
+| 0.43          | 10.0  | 80   | 0.5686          | 0.8871   |
+| 0.3939        | 11.0  | 88   | 0.5312          | 0.9032   |
+| 0.349         | 12.0  | 96   | 0.4888          | 0.9194   |
+| 0.3127        | 13.0  | 104  | 0.4539          | 0.9194   |
+| 0.2806        | 14.0  | 112  | 0.4281          | 0.9194   |
+| 0.2624        | 15.0  | 120  | 0.4062          | 0.9194   |
+| 0.2362        | 16.0  | 128  | 0.3953          | 0.9194   |
+| 0.2231        | 17.0  | 136  | 0.3839          | 0.9194   |
+| 0.2161        | 18.0  | 144  | 0.3799          | 0.9194   |
+| 0.2023        | 19.0  | 152  | 0.3753          | 0.9194   |
+| 0.1982        | 20.0  | 160  | 0.3716          | 0.9194   |
+
+
+### Framework versions
+
+- Transformers 4.47.0.dev0
+- Pytorch 2.4.0+cu121
+- Datasets 3.0.2
+- Tokenizers 0.20.0

config.json

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+{
+  "_name_or_path": "distilbert/distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "negative",
+    "1": "neutral",
+    "2": "positive"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "negative": 0,
+    "neutral": 1,
+    "positive": 2
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0.dev0",
+  "vocab_size": 30522
+}

model.safetensors

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+size 267835644

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": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "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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