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Upload BERT LoRA Shakespeare Q&A model with documentation

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  1. README.md +48 -10
  2. adapter_config.json +38 -0
  3. adapter_model.safetensors +3 -0
  4. bert_lora_training_report.json +112 -0
  5. checkpoint-1000/README.md +202 -0
  6. checkpoint-1000/adapter_config.json +38 -0
  7. checkpoint-1000/adapter_model.safetensors +3 -0
  8. checkpoint-1000/optimizer.pt +3 -0
  9. checkpoint-1000/rng_state.pth +3 -0
  10. checkpoint-1000/scaler.pt +3 -0
  11. checkpoint-1000/scheduler.pt +3 -0
  12. checkpoint-1000/special_tokens_map.json +7 -0
  13. checkpoint-1000/tokenizer.json +0 -0
  14. checkpoint-1000/tokenizer_config.json +56 -0
  15. checkpoint-1000/trainer_state.json +141 -0
  16. checkpoint-1000/training_args.bin +3 -0
  17. checkpoint-1000/vocab.txt +0 -0
  18. checkpoint-1284/README.md +202 -0
  19. checkpoint-1284/adapter_config.json +38 -0
  20. checkpoint-1284/adapter_model.safetensors +3 -0
  21. checkpoint-1284/optimizer.pt +3 -0
  22. checkpoint-1284/rng_state.pth +3 -0
  23. checkpoint-1284/scaler.pt +3 -0
  24. checkpoint-1284/scheduler.pt +3 -0
  25. checkpoint-1284/special_tokens_map.json +7 -0
  26. checkpoint-1284/tokenizer.json +0 -0
  27. checkpoint-1284/tokenizer_config.json +56 -0
  28. checkpoint-1284/trainer_state.json +155 -0
  29. checkpoint-1284/training_args.bin +3 -0
  30. checkpoint-1284/vocab.txt +0 -0
  31. checkpoint-500/README.md +202 -0
  32. checkpoint-500/adapter_config.json +38 -0
  33. checkpoint-500/adapter_model.safetensors +3 -0
  34. checkpoint-500/optimizer.pt +3 -0
  35. checkpoint-500/rng_state.pth +3 -0
  36. checkpoint-500/scaler.pt +3 -0
  37. checkpoint-500/scheduler.pt +3 -0
  38. checkpoint-500/special_tokens_map.json +7 -0
  39. checkpoint-500/tokenizer.json +0 -0
  40. checkpoint-500/tokenizer_config.json +56 -0
  41. checkpoint-500/trainer_state.json +92 -0
  42. checkpoint-500/training_args.bin +3 -0
  43. checkpoint-500/vocab.txt +0 -0
  44. special_tokens_map.json +7 -0
  45. tokenizer.json +0 -0
  46. tokenizer_config.json +56 -0
  47. vocab.txt +0 -0
README.md CHANGED
@@ -1,10 +1,48 @@
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- ---
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- license: mit
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- datasets:
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- - Hananguyen12/shakespeare-QA-plays
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- language:
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- - en
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- base_model:
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- - google-bert/bert-base-uncased
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- pipeline_tag: text-generation
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # BERT LoRA Shakespeare Q&A Model
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+
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+ This model is fine-tuned using LoRA (Low-Rank Adaptation) on BERT Base Uncased for Shakespeare question answering.
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import BertTokenizerFast, BertForQuestionAnswering
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+ from peft import PeftModel
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+
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+ # Load model
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+ tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
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+ base_model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
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+ model = PeftModel.from_pretrained(base_model, "Hananguyen12/bert-base-uncase-lora-shakespeare-plays")
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+
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+ # Ask questions
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+ question = "Who is Romeo?"
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+ context = "Romeo Montague is a young man from the Montague family in Verona..."
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+
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+ inputs = tokenizer(question, context, return_tensors="pt")
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+ outputs = model(**inputs)
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+
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+ start_idx = outputs.start_logits.argmax()
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+ end_idx = outputs.end_logits.argmax()
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+ answer = tokenizer.decode(inputs['input_ids'][0][start_idx:end_idx+1])
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+ ```
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+
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+ ## Model Details
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+
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+ - **Base Model**: bert-base-uncased
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+ - **Fine-tuning**: LoRA adaptation
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+ - **Domain**: Shakespeare's works
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+ - **Task**: Extractive Question Answering
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+
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+ ## Training Data
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+
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+ Trained on comprehensive Shakespeare Q&A dataset covering:
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+ - Character analysis
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+ - Plot understanding
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+ - Thematic questions
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+ - Literary interpretation
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+
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+ ## Use Cases
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+
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+ - Educational tools for Shakespeare studies
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+ - Literature analysis
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+ - Student homework assistance
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+ - Digital humanities research
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+ {
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+ "configuration": {
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+ "base_model_name": "bert-base-uncased",
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+ "gradient_accumulation_steps": 4,
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+ "learning_rate": 0.0002,
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+ "num_epochs": 4,
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+ "warmup_steps": 500,
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+ "dataset_statistics": {
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+ "total_examples": 6032,
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+ "train_examples": 5127,
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+ "val_examples": 603,
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+ "test_examples": 302,
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+ "question_types": {
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+ "factual": 4425,
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+ "quote": 1520,
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+ "analysis": 75,
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+ "model_statistics": {
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+ "total_parameters": 111548932,
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+ "trainable_parameters": 2655746,
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+ "trainable_percentage": 2.3807901630111528,
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+ "parameter_efficiency": "2,655,746 trainable out of 111,548,932 total",
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+ "base_model_path": "/content/shakespeare-bert-base-model",
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+ "lora_adapter_path": "/content/shakespeare-bert-qa-lora"
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+ },
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+ "training_statistics": {
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+ "start_time": "2025-06-04T19:18:02.140488",
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+ "end_time": "2025-06-04T19:30:15.267651",
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+ "max_length": 512,
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+ "effective_batch_size": 16,
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+ "learning_rate": 0.0002
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+ "test_evaluation": {
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+ "eval_start_accuracy": 1.0,
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+ "epoch": 4.0
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+ "validation_evaluation": {
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+ "eval_steps_per_second": 19.391,
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+ "epoch": 4.0
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+ }
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+ },
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+ "system_information": {
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+ "device": "cuda",
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+ "cuda_available": true,
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+ "gpu_name": "Tesla T4",
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+ "gpu_memory_gb": 15.828320256,
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+ "pytorch_version": "2.6.0+cu124",
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+ "transformers_version": "4.35.0+",
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+ "peft_enabled": true
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+ },
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+ "report_generated": "2025-06-04T19:30:27.650635",
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+ "colab_environment": true,
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+ "model_type": "BERT-Base-Uncased with LoRA"
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+ }
checkpoint-1000/README.md ADDED
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+ ---
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+ base_model: bert-base-uncased
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+ library_name: peft
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
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+ - PEFT 0.15.2
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+ ---
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+ base_model: bert-base-uncased
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+ library_name: peft
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+ # Model Card for Model ID
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ ## Training Details
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ ### Framework versions
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+
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+ - PEFT 0.15.2
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+ ---
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+ base_model: bert-base-uncased
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+ library_name: peft
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+ # Model Card for Model ID
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+ ### Recommendations
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ ## Training Details
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ [More Information Needed]
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+ ### Framework versions
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+
202
+ - PEFT 0.15.2
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+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": false,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": true,
47
+ "extra_special_tokens": {},
48
+ "mask_token": "[MASK]",
49
+ "model_max_length": 512,
50
+ "pad_token": "[PAD]",
51
+ "sep_token": "[SEP]",
52
+ "strip_accents": null,
53
+ "tokenize_chinese_chars": true,
54
+ "tokenizer_class": "BertTokenizer",
55
+ "unk_token": "[UNK]"
56
+ }
vocab.txt ADDED
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