End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/deberta-v3-large
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: wikidyk-scope-clf-deberta-v3-large-temporal_10_clusters
+  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. -->
+
+# wikidyk-scope-clf-deberta-v3-large-temporal_10_clusters
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2558
+- Accuracy: 0.9270
+- F1: 0.2310
+- Precision: 0.2810
+- Recall: 0.1961
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- total_train_batch_size: 128
+- total_eval_batch_size: 128
+- optimizer: Use 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: 10.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.0343        | 1.0   | 902  | 0.2095          | 0.9441   | 0.0    | 0.0       | 0.0    |
+| 0.0344        | 2.0   | 1804 | 0.2093          | 0.9441   | 0.0    | 0.0       | 0.0    |
+| 0.0368        | 3.0   | 2706 | 0.2190          | 0.9441   | 0.0    | 0.0       | 0.0    |
+| 0.0396        | 4.0   | 3608 | 0.2225          | 0.9441   | 0.0    | 0.0       | 0.0    |
+| 0.0335        | 5.0   | 4510 | 0.2161          | 0.9441   | 0.0    | 0.0       | 0.0    |
+| 0.0307        | 6.0   | 5412 | 0.2188          | 0.9443   | 0.0105 | 0.75      | 0.0053 |
+| 0.0222        | 7.0   | 6314 | 0.2325          | 0.9422   | 0.0958 | 0.3827    | 0.0548 |
+| 0.0223        | 8.0   | 7216 | 0.2335          | 0.9409   | 0.1601 | 0.3904    | 0.1007 |
+| 0.0173        | 9.0   | 8118 | 0.2529          | 0.9333   | 0.2268 | 0.3225    | 0.1749 |
+| 0.0142        | 10.0  | 9020 | 0.2558          | 0.9270   | 0.2310 | 0.2810    | 0.1961 |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.6.0
+- Tokenizers 0.21.1

added_tokens.json

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config.json

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+{
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
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+    "4": 4,
+    "5": 5,
+    "6": 6,
+    "7": 7,
+    "8": 8,
+    "9": 9
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+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
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+  "layer_norm_eps": 1e-07,
+  "legacy": true,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "problem_type": "multi_label_classification",
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "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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