Hinter-Models/deberta-sber-nli

.gitattributes

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

README.md

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+---
+library_name: transformers
+license: mit
+base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: nli-cross-encoder-roberta
+  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. -->
+
+# nli-cross-encoder-roberta
+
+This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4873
+- Accuracy: 0.9448
+- F1 Macro: 0.9468
+
+## 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: 64
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- 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: cosine
+- lr_scheduler_warmup_ratio: 0.06
+- num_epochs: 8
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
+| 0.2507        | 1.0   | 197  | 0.2485          | 0.9282   | 0.9303   |
+| 0.0926        | 2.0   | 394  | 0.1878          | 0.9420   | 0.9436   |
+| 0.0339        | 3.0   | 591  | 0.3596          | 0.9448   | 0.9468   |
+| 0.0183        | 4.0   | 788  | 0.4495          | 0.9448   | 0.9463   |
+| 0.0185        | 5.0   | 985  | 0.4833          | 0.9392   | 0.9410   |
+| 0.0007        | 6.0   | 1182 | 0.5019          | 0.9365   | 0.9381   |
+| 0.0005        | 7.0   | 1379 | 0.4779          | 0.9475   | 0.9494   |
+| 0.0003        | 8.0   | 1576 | 0.4873          | 0.9448   | 0.9468   |
+
+
+### Framework versions
+
+- Transformers 4.56.1
+- Pytorch 2.6.0+cu124
+- Datasets 4.0.0
+- Tokenizers 0.22.0

added_tokens.json

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+}

config.json

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+    "DebertaV2ForSequenceClassification"
+  ],
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+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "entailment",
+    "1": "neutral",
+    "2": "contradiction"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "contradiction": 2,
+    "entailment": 0,
+    "neutral": 1
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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": 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,
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 0,
+  "vocab_size": 251000
+}

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special_tokens_map.json

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tokenizer_config.json

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