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| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| def getScores(ids, scores, pad_token_id): | |
| """get sequence scores from model.generate output""" | |
| scores = torch.stack(scores, dim=1) | |
| log_probs = torch.log_softmax(scores, dim=2) | |
| # remove start token | |
| ids = ids[:,1:] | |
| # gather needed probs | |
| x = ids.unsqueeze(-1).expand(log_probs.shape) | |
| needed_logits = torch.gather(log_probs, 2, x) | |
| final_logits = needed_logits[:, :, 0] | |
| padded_mask = (ids == pad_token_id) | |
| final_logits[padded_mask] = 0 | |
| final_scores = final_logits.sum(dim=-1) | |
| return final_scores.cpu().detach().numpy() | |
| def topkSample(input, model, tokenizer, | |
| num_samples=5, | |
| num_beams=1, | |
| max_output_length=30): | |
| tokenized = tokenizer(input, return_tensors="pt") | |
| out = model.generate(**tokenized, | |
| do_sample=True, | |
| num_return_sequences = num_samples, | |
| num_beams = num_beams, | |
| eos_token_id = tokenizer.eos_token_id, | |
| pad_token_id = tokenizer.pad_token_id, | |
| output_scores = True, | |
| return_dict_in_generate=True, | |
| max_length=max_output_length,) | |
| out_tokens = out.sequences | |
| out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True) | |
| out_scores = getScores(out_tokens, out.scores, tokenizer.pad_token_id) | |
| pair_list = [(x[0], x[1]) for x in zip(out_str, out_scores)] | |
| sorted_pair_list = sorted(pair_list, key=lambda x:x[1], reverse=True) | |
| return sorted_pair_list | |
| def greedyPredict(input, model, tokenizer): | |
| input_ids = tokenizer([input], return_tensors="pt").input_ids | |
| out_tokens = model.generate(input_ids) | |
| out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True) | |
| return out_str[0] | |
| def predict_tail(entity, relation): | |
| global model, tokenizer | |
| input = entity + "| " + relation | |
| out = topkSample(input, model, tokenizer, num_samples=25) | |
| out_dict = {} | |
| for k, v in out: | |
| out_dict[k] = np.exp(v).item() | |
| return out_dict | |
| tokenizer = AutoTokenizer.from_pretrained("apoorvumang/kgt5-base-wikikg90mv2") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("apoorvumang/kgt5-base-wikikg90mv2") | |
| ent_input = gr.inputs.Textbox(lines=1, default="Apoorv Umang Saxena") | |
| rel_input = gr.inputs.Textbox(lines=1, default="country") | |
| output = gr.outputs.Label() | |
| examples = [ | |
| ['Adrian Kochsiek', 'sex or gender'], | |
| ['Apoorv Umang Saxena', 'family name'], | |
| ['World War II', 'followed by'], | |
| ['Apoorv Umang Saxena', 'country'], | |
| ['Ippolito Boccolini', 'writing language'] , | |
| ['Roelant', 'writing system'] , | |
| ['The Accountant 2227', 'language of work or name'] , | |
| ['Microbial Infection and AMR in Hospitalized Patients With Covid 19', 'study type'] , | |
| ['Carla Fracci', 'manner of death'] , | |
| ['list of programs broadcast by Comet', 'is a list of'] , | |
| ['Loreta Podhradí', 'continent'] , | |
| ['Opistognathotrema', 'taxon rank'] , | |
| ['Museum Arbeitswelt Steyr', 'wheelchair accessibility'] , | |
| ['Heliotropium tytoides', 'subject has role'] , | |
| ['School bus crash rates on routine and nonroutine routes.', 'sponsor'] , | |
| ['Tachigalieae', 'taxon rank'] , | |
| ['Irena Salusová', 'place of detention'] , | |
| ] | |
| title = "Interactive demo: KGT5" | |
| description = """Demo for <a href='https://arxiv.org/abs/2203.10321'>Sequence-to-Sequence Knowledge Graph Completion and Question Answering </a> (KGT5). This particular model is a T5-base model trained on the task of tail prediction on WikiKG90Mv2 dataset and obtains 0.239 validation MRR on this task (<a href="https://ogb.stanford.edu/docs/lsc/leaderboards/#wikikg90mv2">leaderboard</a>, see paper for details). | |
| To use it, simply give an entity name and relation and click 'submit'. Upto 25 model predictions will show up in a few seconds. The model works best when the exact entity/relation names that it has been trained on are used. | |
| It is sometimes able to generalize to unseen entities as well (see examples). | |
| """ | |
| #article = """ | |
| #<p style='text-align: center'><a href='https://arxiv.org/abs/2203.10321'>Sequence-to-Sequence Knowledge Graph Completion and Question Answering </a> | <a href='https://github.com/apoorvumang/kgt5'>Github Repo</a></p> | |
| #""" | |
| article = """ | |
| Under the hood, this demo concatenates the entity and relation, feeds it to the model and then samples 25 sequences, which are then ranked according to their sequence probabilities. | |
| <br> | |
| The text representations of the relations and entities can be downloaded from here: <a href="https://storage.googleapis.com/kgt5-wikikg90mv2/rel_alias_list.pickle">https://storage.googleapis.com/kgt5-wikikg90mv2/rel_alias_list.pickle</a> and | |
| <a href="https://storage.googleapis.com/kgt5-wikikg90mv2/ent_alias_list.pickle">https://storage.googleapis.com/kgt5-wikikg90mv2/ent_alias_list.pickle</a> | |
| <br> | |
| For more details see the <a href='https://github.com/apoorvumang/kgt5'>Github repo</a> or the <a href="https://huggingface.co/apoorvumang/kgt5-base-wikikg90mv2">hf model page</a>. | |
| """ | |
| iface = gr.Interface(fn=predict_tail, | |
| inputs=[ent_input, rel_input], | |
| outputs=output, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples,) | |
| iface.launch() | |