Auto-deploy: ΔF1 >= 0.0010 (v20251030_132015)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+license: mit
+base_model: xlm-roberta-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: urgency_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. -->
+
+# urgency_classifier
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0242
+- Accuracy: 0.9187
+- F1 Macro: 0.9210
+- F1 Weighted: 0.9190
+- Precision Macro: 0.9240
+- Recall Macro: 0.9190
+- Precision Weighted: 0.9203
+- Recall Weighted: 0.9187
+
+## 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: 32
+- 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
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro | Precision Weighted | Recall Weighted |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|:------------------:|:---------------:|
+| 0.0976        | 0.625 | 50   | 0.0433          | 0.7812   | 0.7776   | 0.7678      | 0.7922          | 0.8038       | 0.7983             | 0.7812          |
+| 0.0292        | 1.25  | 100  | 0.0168          | 0.9187   | 0.9206   | 0.9184      | 0.9191          | 0.9231       | 0.9190             | 0.9187          |
+| 0.0232        | 1.875 | 150  | 0.0242          | 0.9187   | 0.9210   | 0.9190      | 0.9240          | 0.9190       | 0.9203             | 0.9187          |
+
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.9.0+cu128
+- Datasets 4.3.0
+- Tokenizers 0.22.1

config.json

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+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "NORMAL",
+    "1": "URGENT",
+    "2": "HIGHLY URGENT"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "HIGHLY URGENT": 2,
+    "NORMAL": 0,
+    "URGENT": 1
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

model.safetensors

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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+  "extra_special_tokens": {},
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+  "tokenizer_class": "XLMRobertaTokenizer",
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+}

training_args.bin

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