Upload HybridQAPipeline
Browse files- README.md +199 -0
- config.json +61 -0
- generation_config.json +7 -0
- hybrid_pipe2.py +111 -0
- model.safetensors +3 -0
- special_tokens_map.json +37 -0
- spiece.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +55 -0
- vocab.txt +0 -0
README.md
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---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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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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## Uses
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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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### Direct Use
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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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[More Information Needed]
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### Downstream Use [optional]
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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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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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[More Information Needed]
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## Training Details
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### Training Data
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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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[More Information Needed]
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### Training Procedure
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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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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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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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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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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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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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- **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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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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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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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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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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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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config.json
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{
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"_name_or_path": "MaRiOrOsSi/t5-base-finetuned-question-answering",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"classifier_dropout": 0.0,
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": false,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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"length_penalty": 2.0,
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"max_length": 200,
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"min_length": 30,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"prefix": "summarize: "
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},
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"translation_en_to_de": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to German: "
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},
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to French: "
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},
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"translation_en_to_ro": {
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"early_stopping": true,
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"max_length": 300,
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"num_beams": 4,
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"prefix": "translate English to Romanian: "
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.40.0",
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"use_cache": true,
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"vocab_size": 32128
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}
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generation_config.json
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{
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"_from_model_config": true,
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"decoder_start_token_id": 0,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.40.0"
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}
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hybrid_pipe2.py
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from hybrid_model import HybridQAModel
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from transformers import QuestionAnsweringPipeline, PretrainedConfig
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForQuestionAnswering
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from transformers import pipeline, PretrainedConfig
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from huggingface_hub import PyTorchModelHubMixin
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import os
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class HybridQAPipeline(QuestionAnsweringPipeline):
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def __init__(self, model=None, tokenizer=None, **kwargs):
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extractive_id = "datarpit/distilbert-base-uncased-finetuned-natural-questions"
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generative_id = "MaRiOrOsSi/t5-base-finetuned-question-answering"
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self.config = HybridQAConfig(extractive_id, generative_id)
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self.model = HybridQAModel(self.config)
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super().__init__(model=self.model, tokenizer=tokenizer, **kwargs)
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self.model = HybridQAModel(self.config)
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def __call__(self, question, context):
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return self.model.predict(question, context)
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class HybridQAConfig(PretrainedConfig):
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def __init__(
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self,
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extractive_id=None,
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generative_id = None,
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**kwargs
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):
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self.extractive_id = extractive_id
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self.generative_id = generative_id
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super().__init__(**kwargs)
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class HybridQAModel(nn.Module, PyTorchModelHubMixin):
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#config_class = HybridQAConfig
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.load_models(config.extractive_id, config.generative_id)
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def can_generate(self):
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return False
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def load_models(self, extractive_id, generative_id):
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50 |
+
self.tokenizer_extractive = AutoTokenizer.from_pretrained(extractive_id)
|
51 |
+
self.tokenizer_generative = AutoTokenizer.from_pretrained(generative_id)
|
52 |
+
|
53 |
+
self.model_extractive = AutoModelForQuestionAnswering.from_pretrained(extractive_id)
|
54 |
+
self.model_generative = AutoModelForSeq2SeqLM.from_pretrained(generative_id)
|
55 |
+
|
56 |
+
def predict(self, question, context):
|
57 |
+
result_gen, conf_gen = self.infer_generative(self.model_generative, self.tokenizer_generative, question)
|
58 |
+
result_ext, conf_ext = self.infer_extractive(self.model_extractive, self.tokenizer_extractive, question, context)
|
59 |
+
|
60 |
+
# print("Generative result: ",result_gen)
|
61 |
+
# print("Confidence: ", conf_gen)
|
62 |
+
# print("Extractive result: ", result_ext)
|
63 |
+
# print("Confidence: ", conf_ext)
|
64 |
+
|
65 |
+
if conf_gen > conf_ext:
|
66 |
+
return {'guess':result_gen, 'confidence':conf_gen}
|
67 |
+
else:
|
68 |
+
return {'guess':result_ext, 'confidence':conf_ext}
|
69 |
+
|
70 |
+
def infer_generative(self, model, tokenizer, input_text, **generate_kwargs):
|
71 |
+
max_input_length = min(tokenizer.model_max_length, model.config.max_length)
|
72 |
+
input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=max_input_length)
|
73 |
+
|
74 |
+
with torch.no_grad():
|
75 |
+
output_ids = model.generate(input_ids, **generate_kwargs)
|
76 |
+
decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
|
77 |
+
|
78 |
+
output_probs = F.softmax(output_ids.float(), dim=-1).squeeze(0)
|
79 |
+
entropy = -(output_probs * torch.log(output_probs)).sum(dim=-1)
|
80 |
+
confidence_score = 1 - entropy.item()
|
81 |
+
|
82 |
+
model.save_pretrained("./base_models")
|
83 |
+
return decoded_output, confidence_score
|
84 |
+
|
85 |
+
def infer_extractive(self, model, tokenizer, question, context):
|
86 |
+
qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
|
87 |
+
result = qa_pipeline(question=question, context=context)
|
88 |
+
confidence_score = result['score']
|
89 |
+
|
90 |
+
model.save_pretrained("./base_models")
|
91 |
+
return result['answer'], confidence_score
|
92 |
+
|
93 |
+
def save_pretrained(self, save_directory, **kwargs):
|
94 |
+
if not os.path.exists(save_directory):
|
95 |
+
os.makedirs(save_directory, exist_ok=True)
|
96 |
+
self.config.save_pretrained(save_directory, **kwargs)
|
97 |
+
self.model_extractive.save_pretrained(save_directory, **kwargs)
|
98 |
+
self.tokenizer_extractive.save_pretrained(save_directory, **kwargs)
|
99 |
+
self.model_generative.save_pretrained(save_directory, **kwargs)
|
100 |
+
self.tokenizer_generative.save_pretrained(save_directory, **kwargs)
|
101 |
+
|
102 |
+
def from_pretrained(cls, save_directory, *model_args, **model_kwargs):
|
103 |
+
config = PretrainedConfig.from_pretrained(save_directory)
|
104 |
+
model = HybridQAModel(config)
|
105 |
+
|
106 |
+
model.model_extractive = AutoModelForQuestionAnswering.from_pretrained(save_directory)
|
107 |
+
model.tokenizer_extractive = AutoTokenizer.from_pretrained(save_directory)
|
108 |
+
model.model_generative = AutoModelForSeq2SeqLM.from_pretrained(save_directory)
|
109 |
+
model.tokenizer_generative = AutoTokenizer.from_pretrained(save_directory)
|
110 |
+
|
111 |
+
return model
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6dc6429963356120d99020672e6e21f121f1d7f64cc8defa5dc5d22c53004713
|
3 |
+
size 891644712
|
special_tokens_map.json
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cls_token": {
|
3 |
+
"content": "[CLS]",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"mask_token": {
|
10 |
+
"content": "[MASK]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"pad_token": {
|
17 |
+
"content": "[PAD]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"sep_token": {
|
24 |
+
"content": "[SEP]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"unk_token": {
|
31 |
+
"content": "[UNK]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
}
|
37 |
+
}
|
spiece.model
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
|
3 |
+
size 791656
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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": true,
|
45 |
+
"cls_token": "[CLS]",
|
46 |
+
"do_lower_case": true,
|
47 |
+
"mask_token": "[MASK]",
|
48 |
+
"model_max_length": 512,
|
49 |
+
"pad_token": "[PAD]",
|
50 |
+
"sep_token": "[SEP]",
|
51 |
+
"strip_accents": null,
|
52 |
+
"tokenize_chinese_chars": true,
|
53 |
+
"tokenizer_class": "DistilBertTokenizer",
|
54 |
+
"unk_token": "[UNK]"
|
55 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|