nhankins/es_euph_distil_3.0

README.md

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+---
+license: apache-2.0
+base_model: distilbert/distilbert-base-multilingual-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+model-index:
+- name: distilbert-base-multilingual-cased-lora-text-classification
+  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. -->
+
+# distilbert-base-multilingual-cased-lora-text-classification
+
+This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5930
+- Precision: 0.7325
+- Recall: 0.7542
+- F1 and accuracy: {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524}
+
+## 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: 1e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy                                            |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
+| No log        | 1.0   | 372  | 0.6533          | 0.6327    | 1.0    | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} |
+| 0.67          | 2.0   | 744  | 0.6432          | 0.6327    | 1.0    | {'accuracy': 0.6327077747989276, 'f1': 0.7750410509031198} |
+| 0.6548        | 3.0   | 1116 | 0.6197          | 0.6341    | 0.9915 | {'accuracy': 0.6327077747989276, 'f1': 0.7735537190082644} |
+| 0.6548        | 4.0   | 1488 | 0.6020          | 0.6678    | 0.8178 | {'accuracy': 0.6273458445040214, 'f1': 0.7352380952380952} |
+| 0.6211        | 5.0   | 1860 | 0.5969          | 0.696     | 0.7373 | {'accuracy': 0.6300268096514745, 'f1': 0.7160493827160493} |
+| 0.5929        | 6.0   | 2232 | 0.5954          | 0.6980    | 0.7542 | {'accuracy': 0.6380697050938338, 'f1': 0.7250509164969451} |
+| 0.5887        | 7.0   | 2604 | 0.5940          | 0.7412    | 0.7161 | {'accuracy': 0.6621983914209115, 'f1': 0.728448275862069}  |
+| 0.5887        | 8.0   | 2976 | 0.5937          | 0.7426    | 0.7458 | {'accuracy': 0.675603217158177, 'f1': 0.7441860465116279}  |
+| 0.5809        | 9.0   | 3348 | 0.5933          | 0.7247    | 0.7585 | {'accuracy': 0.6648793565683646, 'f1': 0.7412008281573499} |
+| 0.5726        | 10.0  | 3720 | 0.5930          | 0.7325    | 0.7542 | {'accuracy': 0.6702412868632708, 'f1': 0.74321503131524}   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "distilbert/distilbert-base-multilingual-cased",
+  "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": 32,
+  "lora_dropout": 0.01,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 4,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_lin",
+    "lin2",
+    "q_lin",
+    "k_lin",
+    "lin1",
+    "out_lin"
+  ],
+  "task_type": "SEQ_CLS",
+  "use_rslora": false
+}

adapter_model.safetensors

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runs/Feb15_20-17-34_e2b6851b2e56/events.out.tfevents.1708028257.e2b6851b2e56.347.0

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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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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "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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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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