eskayML/old_electra_pytranscripts

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: mrm8488/electra-small-finetuned-squadv2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: old_electra_pytranscripts
+  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. -->
+
+# old_electra_pytranscripts
+
+This model is a fine-tuned version of [mrm8488/electra-small-finetuned-squadv2](https://huggingface.co/mrm8488/electra-small-finetuned-squadv2) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.1743
+- Accuracy: 0.2632
+
+## 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: 2
+- eval_batch_size: 2
+- 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: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 76   | 2.2813          | 0.2368   |
+| No log        | 2.0   | 152  | 2.2611          | 0.1579   |
+| No log        | 3.0   | 228  | 2.2412          | 0.2368   |
+| No log        | 4.0   | 304  | 2.2222          | 0.2632   |
+| No log        | 5.0   | 380  | 2.2071          | 0.2632   |
+| No log        | 6.0   | 456  | 2.1922          | 0.2632   |
+| 2.1966        | 7.0   | 532  | 2.1866          | 0.2368   |
+| 2.1966        | 8.0   | 608  | 2.1814          | 0.2105   |
+| 2.1966        | 9.0   | 684  | 2.1775          | 0.2632   |
+| 2.1966        | 10.0  | 760  | 2.1743          | 0.2632   |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0

config.json

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+{
+  "_name_or_path": "mrm8488/electra-small-finetuned-squadv2",
+  "architectures": [
+    "ElectraForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 128,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "Value equation",
+    "1": "labels",
+    "2": "Financial Impact",
+    "3": "Health System Characteristics",
+    "4": "Clinical utility & efficiency-Provider perspective",
+    "5": "Workflow related problems",
+    "6": "Provider Characteristics",
+    "7": "Training",
+    "8": "Patient/Physician interaction in LUS",
+    "9": "Imaging modalities in general"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "label2id": {
+    "Clinical utility & efficiency-Provider perspective": 4,
+    "Financial Impact": 2,
+    "Health System Characteristics": 3,
+    "Imaging modalities in general": 9,
+    "Patient/Physician interaction in LUS": 8,
+    "Provider Characteristics": 6,
+    "Training": 7,
+    "Value equation": 0,
+    "Workflow related problems": 5,
+    "labels": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:8d431f10ed5a5cd25ebefd3b4ffdf9bf41249fc414fd1abe24bc0eece665c526
+size 54229432

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": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "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,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "ElectraTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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