
# Model Card for T5-LM-Large_Canard-HotpotQA-rephrase 

This model is trained on three objectives: (1) Generating answers for Canard dataset, (2) Generating answers for HotpotQA, (3) Rephrasing questions by the previous conversations of Canard.

## Training

The model was trained using the following script, exported from the corresponding Jupyter notebook. All details, including the request format, can be inferred without errors from the code.
The best checkpoint was picked by a minimal loss on all (3) training objectives.

```python
import datasets
canard_train_augm = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="train")  # see the dataset card for details
canard_test_augm = datasets.load_dataset("gaussalgo/Canard_Wiki-augmented", split="test")

canard_df = canard_train_augm.to_pandas()
canard_test_df = canard_train_augm.to_pandas()

### Curation of seq2seq input contexts and labels
import random

def input_context_from_sample(row: dict, max_length=5) -> str:
    context = "Previous conversation:"
    context += "\nQuestion: "
    context += ", ".join(row["History"][:3])
    for i in range(3, len(row["History"]), 2):
        context += "\nAnswer: "
        context += row["History"][i]
        if i+1 < len(row["History"]):
            context += "\nQuestion: "
            context += row["History"][i+1]

    context += "\n\nCurrent Question: "
    context += row["Question"]

    context += "\nSearch results:"
    all_contexts = row["retrieved_contexts"].tolist()[:max_length-1] + [row["true_contexts"]]
    random.shuffle(all_contexts)

    for i, search_result in enumerate(all_contexts):
        context += "\n[%s]: " % (i+1)
        context += search_result.replace("CANNOTANSWER", "")

    context += "\nCurrent Answer: "
    return context


def rephrasing_context_from_sample(row: dict) -> str:
    context = "Previous conversation:"
    context += "\nQuestion: "
    context += ", ".join(row["History"][:3])
    for i in range(3, len(row["History"]), 2):
        context += "\nAnswer: "
        context += row["History"][i]
        if i+1 < len(row["History"]):
            context += "\nQuestion: "
            context += row["History"][i+1]
    
    context += "\n\nCurrent Question: "
    context += row["Question"]

    context += "\nMore specific question: "
    return context


def hotpotqa_context(row: dict) -> str:
    context = "Current Question: "
    context += row["question"]

    context += "\nSearch results:"
    all_contexts = [" ".join(context) for context in row["context"]["sentences"]]

    for i, search_result in enumerate(all_contexts):
        context += "\n[%s]: " % (i+1)
        # context += search_result.replace("CANNOTANSWER", "")

    context += "\nCurrent Answer: "
    return context


input_texts = canard_df.apply(lambda row: input_context_from_sample(row), axis=1).values
input_val_texts = canard_test_df.iloc[:200].apply(lambda row: input_context_from_sample(row), axis=1).values

too_long_index = [len(t) > 20000 for t in input_texts]
input_texts = [t for i, t in enumerate(input_texts) if not too_long_index[i]]
print("training on %s samples" % len(input_texts))

labels = canard_df.answer.apply(lambda ans: "No answer" if ans == "CANNOTANSWER" else ans).values
labels = [l for i, l in enumerate(labels)  if not too_long_index[i]]

val_labels = canard_test_df.answer.apply(lambda ans: "No answer" if ans == "CANNOTANSWER" else ans).values

rephrasing_inputs = canard_df.apply(lambda row: rephrasing_context_from_sample(row), axis=1).values
print(rephrasing_inputs[0])

rephrasing_val_inputs = canard_test_df.apply(lambda row: rephrasing_context_from_sample(row), axis=1).values

rephrasing_labels = canard_df.Rewrite.values
rephrasing_val_labels = canard_test_df.Rewrite.values
print(rephrasing_labels[0])

# Training
# see Adaptor's homepage for details:
# https://github.com/gaussalgo/adaptor

from adaptor.lang_module import LangModule

lang_module = LangModule("google/t5-large-lm-adapt")

from adaptor.evaluators.generative import ROUGE, BLEU

evaluators = [BLEU(), ROUGE()]

from adaptor.objectives.seq2seq import Sequence2Sequence

seq_qa = Sequence2Sequence(lang_module,
                           texts_or_path=input_texts,
                           labels_or_path=labels,
                           val_texts_or_path=input_val_texts,
                           val_labels_or_path=val_labels,
                           batch_size=4,
                           val_evaluators=evaluators,
                           objective_id="Canard")

hotpot_train = datasets.load_dataset("hotpot_qa", "distractor")["train"]
hotpot_val = datasets.load_dataset("hotpot_qa", "distractor")["validation"]

hotpot_inputs = hotpot_train.to_pandas().apply(hotpotqa_context, axis=1)
hotpot_val_inputs = hotpot_val.to_pandas().apply(hotpotqa_context, axis=1)

too_long_index = [len(t) > 20000 for t in hotpot_inputs]

hotpot_inputs = [t for i, t in enumerate(hotpot_inputs) if not too_long_index[i]]
hotpot_answers = [t for i, t in enumerate(hotpot_train["answer"]) if not too_long_index[i]]

seq_additional_qa = Sequence2Sequence(lang_module,
                                      texts_or_path=hotpot_inputs,
                                      labels_or_path=hotpot_answers,
                                      val_texts_or_path=hotpot_val_inputs[:200],
                                      val_labels_or_path=hotpot_val["answer"][:200],
                                      batch_size=4,
                                      val_evaluators=evaluators,
                                      objective_id="HotpotQA",
                                      share_other_objective_head=seq_qa)


seq_rephrasing = Sequence2Sequence(lang_module,
                                   texts_or_path=rephrasing_inputs,
                                   labels_or_path=rephrasing_labels,
                                   val_texts_or_path=rephrasing_val_inputs[:200],
                                   val_labels_or_path=rephrasing_val_labels[:200],
                                   batch_size=4,
                                   val_evaluators=evaluators,
                                   objective_id="rephrasing",
                                   share_other_objective_head=seq_qa)

from adaptor.utils import AdaptationArguments, StoppingStrategy

training_arguments = AdaptationArguments(output_dir="checkpoints-chatbot",
                                         learning_rate=5e-5,
                                         stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
                                         stopping_patience=8,
                                         save_total_limit=8,
                                         do_train=True,
                                         do_eval=True,
                                         bf16=True,
                                         warmup_steps=1000,
                                         gradient_accumulation_steps=8,
                                         logging_steps=10,
                                         eval_steps=200,
                                         save_steps=1000,
                                         num_train_epochs=10,
                                         evaluation_strategy="steps")

from adaptor.schedules import ParallelSchedule
from adaptor.adapter import Adapter

schedule = ParallelSchedule(objectives=[seq_qa, seq_additional_qa, seq_rephrasing],
                            args=training_arguments)
adapter = Adapter(lang_module, schedule, args=training_arguments)

adapter.train()
```


## Usage

See the prompting templates used in training to infer the optimal prompting format.

#### Contact

Feel free to ask questions here, or at stefanik{at} gaussalgo.com
{"datasets": ["hotpot_qa", "gaussalgo/Canard_Wiki-augmented"]}
transformers pytorch safetensors t5 text2text-generation dataset:hotpot_qa dataset:gaussalgo/Canard_Wiki-augmented autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #dataset-hotpot_qa #dataset-gaussalgo/Canard_Wiki-augmented #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# Model Card for T5-LM-Large_Canard-HotpotQA-rephrase 

This model is trained on three objectives: (1) Generating answers for Canard dataset, (2) Generating answers for HotpotQA, (3) Rephrasing questions by the previous conversations of Canard.

## Training

The model was trained using the following script, exported from the corresponding Jupyter notebook. All details, including the request format, can be inferred without errors from the code.
The best checkpoint was picked by a minimal loss on all (3) training objectives.




## Usage

See the prompting templates used in training to infer the optimal prompting format.

#### Contact

Feel free to ask questions here, or at stefanik{at} URL
# Model Card for T5-LM-Large_Canard-HotpotQA-rephrase 

This model is trained on three objectives: (1) Generating answers for Canard dataset, (2) Generating answers for HotpotQA, (3) Rephrasing questions by the previous conversations of Canard. ## Training

The model was trained using the following script, exported from the corresponding Jupyter notebook. All details, including the request format, can be inferred without errors from the code.
The best checkpoint was picked by a minimal loss on all (3) training objectives. ## Usage

See the prompting templates used in training to infer the optimal prompting format. #### Contact

Feel free to ask questions here, or at stefanik{at} URL
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #dataset-hotpot_qa #dataset-gaussalgo/Canard_Wiki-augmented #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for T5-LM-Large_Canard-HotpotQA-rephrase 

This model is trained on three objectives: (1) Generating answers for Canard dataset, (2) Generating answers for HotpotQA, (3) Rephrasing questions by the previous conversations of Canard. ## Training

The model was trained using the following script, exported from the corresponding Jupyter notebook. All details, including the request format, can be inferred without errors from the code.
The best checkpoint was picked by a minimal loss on all (3) training objectives. ## Usage

See the prompting templates used in training to infer the optimal prompting format. #### Contact

Feel free to ask questions here, or at stefanik{at} URL
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #dataset-hotpot_qa #dataset-gaussalgo/Canard_Wiki-augmented #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Model Card for T5-LM-Large_Canard-HotpotQA-rephrase 

This model is trained on three objectives: (1) Generating answers for Canard dataset, (2) Generating answers for HotpotQA, (3) Rephrasing questions by the previous conversations of Canard.## Training

The model was trained using the following script, exported from the corresponding Jupyter notebook. All details, including the request format, can be inferred without errors from the code.
The best checkpoint was picked by a minimal loss on all (3) training objectives.## Usage

See the prompting templates used in training to infer the optimal prompting format.#### Contact

Feel free to ask questions here, or at stefanik{at} URL