k011/startup-eligibility

README.md

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+---
+license: apache-2.0
+base_model: albert/albert-base-v2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: startup-score
+  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. -->
+
+# startup-score
+
+This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7827
+- Accuracy: 0.25
+- F1: 0.3000
+- Precision: 0.375
+- Recall: 0.25
+- Accuracy Label Eligible: 0.0
+- Accuracy Label Not eligible: 0.3333
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- 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: 500
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "albert/albert-base-v2",
+  "architectures": [
+    "AlbertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu_new",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "ELIGIBLE",
+    "1": "NOT ELIGIBLE"
+  },
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "label2id": {
+    "ELIGIBLE": 0,
+    "NOT ELIGIBLE": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 12,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "vocab_size": 30000
+}

model.safetensors

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runs/Jun16_07-30-17_236257c5c870/events.out.tfevents.1718523017.236257c5c870.405.0

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runs/Jun16_07-30-17_236257c5c870/events.out.tfevents.1718523577.236257c5c870.405.1

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special_tokens_map.json

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tokenizer_config.json

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+  "added_tokens_decoder": {
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "AlbertTokenizer",
+  "unk_token": "<unk>"
+}

trainer_state.json

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+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 3.0,
+  "eval_steps": 100,
+  "global_step": 9,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
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+      "train_steps_per_second": 0.017
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+      "eval_accuracy": 0.25,
+      "eval_accuracy_label_ELIGIBLE": 0.0,
+      "eval_accuracy_label_NOT ELIGIBLE": 0.3333333333333333,
+      "eval_f1": 0.30000000000000004,
+      "eval_loss": 0.7826551795005798,
+      "eval_precision": 0.375,
+      "eval_recall": 0.25,
+      "eval_runtime": 5.0134,
+      "eval_samples_per_second": 1.596,
+      "eval_steps_per_second": 0.199,
+      "step": 9
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+  "logging_steps": 10,
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+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 1000,
+  "stateful_callbacks": {
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+  "trial_params": null
+}

training_args.bin

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