Uploading crypto sustainability 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_sustainability_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_sustainability_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: 0.0678
+- Accuracy: 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: 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.0723        | 1.0   | 8    | 1.0659          | 0.2167   |
+| 1.0079        | 2.0   | 16   | 0.9776          | 0.75     |
+| 0.908         | 3.0   | 24   | 0.8537          | 0.9333   |
+| 0.7741        | 4.0   | 32   | 0.7082          | 0.9667   |
+| 0.6449        | 5.0   | 40   | 0.5749          | 0.9667   |
+| 0.5218        | 6.0   | 48   | 0.4551          | 0.9667   |
+| 0.4026        | 7.0   | 56   | 0.3505          | 1.0      |
+| 0.3144        | 8.0   | 64   | 0.2714          | 1.0      |
+| 0.2424        | 9.0   | 72   | 0.2163          | 1.0      |
+| 0.1939        | 10.0  | 80   | 0.1709          | 1.0      |
+| 0.1559        | 11.0  | 88   | 0.1384          | 1.0      |
+| 0.1247        | 12.0  | 96   | 0.1169          | 1.0      |
+| 0.1073        | 13.0  | 104  | 0.0992          | 1.0      |
+| 0.094         | 14.0  | 112  | 0.0890          | 1.0      |
+| 0.0823        | 15.0  | 120  | 0.0813          | 1.0      |
+| 0.0759        | 16.0  | 128  | 0.0756          | 1.0      |
+| 0.0708        | 17.0  | 136  | 0.0720          | 1.0      |
+| 0.0684        | 18.0  | 144  | 0.0697          | 1.0      |
+| 0.0668        | 19.0  | 152  | 0.0683          | 1.0      |
+| 0.0634        | 20.0  | 160  | 0.0678          | 1.0      |
+
+
+### 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
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "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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