Finished training.

README.md

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+---
+license: apache-2.0
+library_name: peft
+tags:
+- parquet
+- text-classification
+datasets:
+- tweet_eval
+metrics:
+- accuracy
+base_model: cross-encoder/ms-marco-MiniLM-L-4-v2
+model-index:
+- name: cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: tweet_eval
+      type: tweet_eval
+      config: emotion
+      split: validation
+      args: emotion
+    metrics:
+    - type: accuracy
+      value: 0.4358288770053476
+      name: accuracy
+---
+
+<!-- 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. -->
+
+# cross-encoder_ms-marco-MiniLM-L-4-v2-finetuned-lora-tweet_eval_emotion
+
+This model is a fine-tuned version of [cross-encoder/ms-marco-MiniLM-L-4-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-4-v2) on the tweet_eval dataset.
+It achieves the following results on the evaluation set:
+- accuracy: 0.4358
+
+## 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: 0.0004
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4
+
+### Training results
+
+| accuracy | train_loss | epoch |
+|:--------:|:----------:|:-----:|
+| 0.2380   | None       | 0     |
+| 0.4278   | 1.2772     | 0     |
+| 0.4278   | 1.2622     | 1     |
+| 0.4385   | 1.2263     | 2     |
+| 0.4358   | 1.2005     | 3     |
+
+
+### Framework versions
+
+- PEFT 0.8.2
+- Transformers 4.37.2
+- Pytorch 2.2.0
+- Datasets 2.16.1
+- Tokenizers 0.15.2

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "cross-encoder/ms-marco-MiniLM-L-4-v2",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 1,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 1,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "query",
+    "value"
+  ],
+  "task_type": "SEQ_CLS",
+  "use_rslora": false
+}

adapter_model.safetensors

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+oid sha256:428b733cecd96ead2e0e18782e22398da4d1f77a9f5dbad8ac2b266fbe33dc17
+size 33128

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}