| from typing import Dict, List, Any |
| from scipy.special import softmax |
|
|
| from utils import clean_str, clean_str_nopunct |
| import torch |
| from transformers import BertTokenizer |
| from utils import MultiHeadModel, BertInputBuilder, get_num_words |
|
|
| MODEL_CHECKPOINT='ddemszky/uptake-model' |
|
|
| class EndpointHandler(): |
| def __init__(self, path="."): |
| print("Loading models...") |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" |
| self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") |
| self.input_builder = BertInputBuilder(tokenizer=self.tokenizer) |
| self.model = MultiHeadModel.from_pretrained(path, head2size={"nsp": 2}) |
| self.model.to(self.device) |
| self.max_length = 120 |
|
|
| def get_clean_text(self, text, remove_punct=False): |
| if remove_punct: |
| return clean_str_nopunct(text) |
| return clean_str(text) |
|
|
| def get_prediction(self, instance): |
| instance["attention_mask"] = [[1] * len(instance["input_ids"])] |
| for key in ["input_ids", "token_type_ids", "attention_mask"]: |
| instance[key] = torch.tensor(instance[key]).unsqueeze(0).to(self.device) |
|
|
| output = self.model(input_ids=instance["input_ids"], |
| attention_mask=instance["attention_mask"], |
| token_type_ids=instance["token_type_ids"], |
| return_pooler_output=False) |
| return output |
|
|
| def get_uptake_score(self, textA, textB): |
|
|
| textA = self.get_clean_text(textA, remove_punct=False) |
| textB = self.get_clean_text(textB, remove_punct=False) |
|
|
| instance = self.input_builder.build_inputs([textA], textB, |
| max_length=self.max_length, |
| input_str=True) |
| output = self.get_prediction(instance) |
| uptake_score = softmax(output["nsp_logits"][0].tolist())[1] |
| return uptake_score |
|
|
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| """ |
| data args: |
| utterances (:obj: `list`) |
| parameters (:obj: `dict`) |
| Return: |
| A :obj:`list` | `dict`: will be serialized and returned |
| """ |
| |
| utterances = data.pop("inputs", data) |
| params = data.pop("parameters", None) |
|
|
| print("EXAMPLES") |
| for utt in utterances[:3]: |
| print("speaker %s: %s" % (utt["speaker"], utt["text"])) |
|
|
| print("Running inference on %d examples..." % len(utterances)) |
| self.model.eval() |
| prev_num_words = 0 |
| prev_text = "" |
| uptake_scores = {} |
| with torch.no_grad(): |
| for i, utt in enumerate(utterances): |
| if utt["speaker"] == params["speaker_2"] and (prev_num_words >= params["speaker_1_min_num_words"]): |
| uptake_scores[str(utt["id"])] = self.get_uptake_score(textA=prev_text, textB=utt["text"]) |
| prev_num_words = get_num_words(utt["text"]) |
| prev_text = utt["text"] |
|
|
| return uptake_scores |
