End of training

README.md

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+---
+license: mit
+base_model: OpenAssistant/reward-model-deberta-v3-large-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: NLP_A2_alt
+  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. -->
+
+# NLP_A2_alt
+
+This model is a fine-tuned version of [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3513
+- Accuracy: 0.8834
+
+## 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: 6e-06
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| No log        | 0.9988 | 419  | 0.4063          | 0.8767   |
+| 0.0925        | 1.9976 | 838  | 0.3513          | 0.8834   |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.1.2
+- Datasets 2.18.0
+- Tokenizers 0.19.1

added_tokens.json

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+{
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+}

config.json

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+{
+  "_name_or_path": "OpenAssistant/reward-model-deberta-v3-large-v2",
+  "architectures": [
+    "DebertaV2ForMultipleChoice"
+  ],
+  "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,
+  "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": "regression",
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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runs/Jun05_21-24-40_a4f63cbe6cef/events.out.tfevents.1717622682.a4f63cbe6cef.106.0

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runs/Jun05_22-10-41_a4f63cbe6cef/events.out.tfevents.1717625441.a4f63cbe6cef.106.1

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special_tokens_map.json

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+  "sep_token": "[SEP]",
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+    "lstrip": false,
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spm.model

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tokenizer_config.json

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

training_args.bin

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