Training in progress, epoch 1

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: unga-climate-classifier
+  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. -->
+
+# unga-climate-classifier
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0936
+- Accuracy: 0.9798
+- F1 Macro: 0.9765
+- Accuracy Balanced: 0.9751
+- F1 Micro: 0.9798
+- Precision Macro: 0.9780
+- Recall Macro: 0.9751
+- Precision Micro: 0.9798
+- Recall Micro: 0.9798
+
+## 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: 80
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
+| No log        | 1.0   | 123  | 0.1778          | 0.9609   | 0.9543   | 0.9510            | 0.9609   | 0.9577          | 0.9510       | 0.9609          | 0.9609       |
+| No log        | 2.0   | 246  | 0.1614          | 0.9680   | 0.9626   | 0.9593            | 0.9680   | 0.9661          | 0.9593       | 0.9680          | 0.9680       |
+| No log        | 3.0   | 369  | 0.1598          | 0.9680   | 0.9626   | 0.9593            | 0.9680   | 0.9661          | 0.9593       | 0.9680          | 0.9680       |
+| No log        | 4.0   | 492  | 0.1191          | 0.9703   | 0.9653   | 0.9610            | 0.9703   | 0.9699          | 0.9610       | 0.9703          | 0.9703       |
+| 0.1357        | 5.0   | 615  | 0.1400          | 0.9727   | 0.9681   | 0.9638            | 0.9727   | 0.9727          | 0.9638       | 0.9727          | 0.9727       |
+
+
+### Framework versions
+
+- Transformers 4.36.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.6.0
+- Tokenizers 0.15.1

added_tokens.json

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+}

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-base",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "other",
+    "1": "climate"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "climate": 1,
+    "other": 0
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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+  "sp_model_kwargs": {},
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+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}

training_args.bin

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