Rjp97/sts-microsoft-deberta-v3-base

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/deberta-v3-large
+tags:
+- generated_from_trainer
+model-index:
+- name: microsoft-deberta-v3-large
+  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. -->
+
+# microsoft-deberta-v3-large
+
+This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5698
+- Pearson R: 0.8241
+- Spearman R: 0.8276
+
+## 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: 0.0005
+- train_batch_size: 8
+- eval_batch_size: 64
+- 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: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Pearson R | Spearman R |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:----------:|
+| 0.6835        | 1.0   | 719  | 0.6543          | 0.5224    | 0.5221     |
+| 0.6489        | 2.0   | 1438 | 0.6053          | 0.7743    | 0.7848     |
+| 0.6408        | 3.0   | 2157 | 0.6005          | 0.7826    | 0.7938     |
+| 0.6288        | 4.0   | 2876 | 0.5970          | 0.7778    | 0.7792     |
+| 0.6138        | 5.0   | 3595 | 0.6643          | 0.7622    | 0.7814     |
+| 0.6044        | 6.0   | 4314 | 0.6048          | 0.8059    | 0.8135     |
+| 0.597         | 7.0   | 5033 | 0.5865          | 0.8243    | 0.8307     |
+| 0.5946        | 8.0   | 5752 | 0.5702          | 0.8170    | 0.8236     |
+| 0.5915        | 9.0   | 6471 | 0.5738          | 0.8211    | 0.8242     |
+| 0.5891        | 10.0  | 7190 | 0.5698          | 0.8241    | 0.8276     |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

added_tokens.json

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+{
+  "[MASK]": 128000
+}

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-large",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

hyperparameters.txt

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+{"learning_rate": 0.0005, "per_device_train_batch_size": 8, "weight_decay": 0.01}

metrics.txt

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+{"train_loss": 0.575105607509613, "train_pearson_r": 0.8384653329849243, "train_spearman_r": 0.8202532676029, "validation_loss": 0.5865102410316467, "validation_pearson_r": 0.8242563605308533, "validation_spearman_r": 0.8307239203986149, "test_loss": 0.570310652256012, "test_pearson_r": 0.8372111916542053, "test_spearman_r": 0.8313447709347793}

model.safetensors

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runs/Feb28_11-01-05_bress145-13/events.out.tfevents.1740741751.bress145-13

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special_tokens_map.json

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+{
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+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spm.model

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tokenizer_config.json

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

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