def prepro(ds, fold, num_tokens_per_group, num_paragraphs, pad=True, n_samples=None): fold_funcs = { 'train': lambda: ds.get_train(), 'dev': lambda: ds.get_dev(), 'test': lambda: ds.get_test() } qs = fold_funcs[fold]() if n_samples is not None: qs = qs[:n_samples] evidence = ds.evidence prep = None extract = ExtractMultiParagraphsPerQuestion( MergeParagraphs(num_tokens_per_group), ShallowOpenWebRanker(num_paragraphs), prep, intern=True) answers = {} batches = {} for q in tqdm(qs, ncols=80, desc='preprocessing'): pre = extract.preprocess([q], evidence) if len(pre.data) == 0: continue assert len(pre.data) < 2 assert q.question_id not in answers assert q.question_id not in batches mpq = pre.data[0] pq_batch = [ ParagraphAndQuestion(p.get_context(), q.question, None, q.question_id, p.doc_id) for p in mpq.paragraphs # document paragraph question? ] if pad: for i in range(num_paragraphs - len(pq_batch)): pq_batch.append( ParagraphAndQuestion([], q.question, None, q.question_id, None)) answers[q.question_id] = mpq.answer_text batches[q.question_id] = pq_batch voc = {w for bs in batches.values() for b in bs for w in b.question} voc.update({w for bs in batches.values() for b in bs for w in b.context}) return answers, batches, voc
def _get_span_scores(self, question: List[str], paragraphs: List[ParagraphWithInverse]): """ Answer a question using the given paragraphs, returns both the span scores and the pre-processed paragraphs the span are valid for """ if self.model.preprocessor is not None: prepped = [] for para in paragraphs: if hasattr(para, "spans"): spans = para.spans else: spans = None text, _, inv = self.model.preprocessor.encode_paragraph( [], para.text, para.start == 0, np.zeros((0, 2), dtype=np.int32), spans) prepped.append( WebParagraph([text], para.original_text, inv, para.paragraph_num, para.start, para.end, para.source_name, para.source_url)) paragraphs = prepped qa_pairs = [ ParagraphAndQuestion(c.get_context(), question, None, "") for c in paragraphs ] encoded = self.model.encode(qa_pairs, False) return self.sess.run(self.span_scores, encoded), paragraphs
def get_document_embedding(self, text): document_tokens, _ = self.tokenize(text) test_question = ParagraphAndQuestion(document_tokens, ['dummy', 'question'], None, "cape_question", 'cape_document') feed = self.model.encode([test_question], False, cached_doc=None) return self.sess.run(self.model.context_rep, feed_dict=feed)[0]
def read_input_data(model): data = [] vocab = set() tokenizer = NltkAndPunctTokenizer() splitter = Truncate(400) # NOTE: we truncate past 400 tokens selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5) with open(OPTS.input_file) as f: for i, line in enumerate(f): try: document_raw, question_raw = line.strip().split('\t') except ValueError as e: print(line.strip()) print('Error at line %d' % i) raise e document = re.split("\s*\n\s*", document_raw) question = tokenizer.tokenize_paragraph_flat(question_raw) doc_toks = [tokenizer.tokenize_paragraph(p) for p in document] split_doc = splitter.split(doc_toks) context = selector.prune(question, split_doc) if model.preprocessor is not None: context = [model.preprocessor.encode_text(question, x) for x in context] else: context = [flatten_iterable(x.text) for x in context] vocab.update(question) for txt in context: vocab.update(txt) ex = [ParagraphAndQuestion(x, question, None, "user-question%d"%i) for i, x in enumerate(context)] data.append((document_raw, question_raw, context, ex)) return data, vocab
def build_dataset(self, data: FilteredData, corpus) -> Dataset: flattened = [] for point in data.data: for para in point.paragraphs: flattened.append(ParagraphAndQuestion(para.text, point.question, TokenSpans(point.answer_text, para.answer_spans), point.question_id)) return ParagraphAndQuestionDataset(flattened, self.batcher)
def build_qa_pair(self, question, question_id, answer_text, group=None) -> ContextAndQuestion: if answer_text is None: ans = None elif group is None: ans = TokenSpans(answer_text, self.answer_spans) else: ans = TokenSpanGroup(answer_text, self.answer_spans, group) return ParagraphAndQuestion(self.text, question, ans, question_id)
def _get_span_scores(self, question: List[str], paragraphs: List[WebParagraph]): paragraphs = self._preprocess(paragraphs) qa_pairs = [ ParagraphAndQuestion(c.get_context(), question, None, "") for c in paragraphs ] encoded = self.model.encode(qa_pairs, False) return self.sess.run(self.span_scores, encoded), paragraphs
def get_logits(self, question, document_embedding): question_tokens, _ = self.tokenize(question) n_words = document_embedding.shape[0] dummy_document = ['dummy'] * n_words test_question = ParagraphAndQuestion(dummy_document, question_tokens, None, "cape_question", 'cape_document') feed = self.model.encode( [test_question], False, cached_doc=document_embedding[np.newaxis, :, :]) start_logits, end_logits = self.sess.run( [self.start_logits, self.end_logits], feed_dict=feed) return start_logits[0][:n_words], end_logits[0][:n_words]
def get_answers_faster(sess, model, pred, batches, answers, topk, batch_size, verbose=False): mr_answers = {} batch_acc = [] location_acc = [] logits_dict = {} def _run_batch(batch_acc, location_acc): feed = model.encode(batch_acc, False) yp, yp2 = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed) qqids, js = zip(*location_acc) for (qqid, j, stl, enl) in zip(qqids, js, yp[:len(location_acc)], yp2[:len(location_acc)]): logits_dict[(qqid, j)] = (stl, enl) for qid, batch in tqdm(batches.items()): for i, b in enumerate(batch): batch_acc.append(b) location_acc.append((qid, i)) if len(batch_acc) == batch_size: _run_batch(batch_acc, location_acc) batch_acc, location_acc = [], [] if len(batch_acc) != 0: bs = len(batch_acc) for _ in range(batch_size - bs): batch_acc.append(ParagraphAndQuestion([], [], None, None, None)) _run_batch(batch_acc, location_acc) qids = batches.keys() if verbose else tqdm( batches.keys(), ncols=80, desc='Extracting answers') for qid in qids: batch = batches[qid] yp = [logits_dict[qid, j][0] for j in range(len(batch))] yp2 = [logits_dict[qid, j][1] for j in range(len(batch))] mr_answers[qid] = _decode_answers(qid, batch, yp, yp2, answers, topk, verbose) return mr_answers
def find_answer(documents, raw_question): raw_question = raw_question.lower() documents = [d.lower() for d in documents] global best_spans, conf documents = [re.split("\s*\n\s*", doc) for doc in documents] tokenizer = NltkAndPunctTokenizer() question = tokenizer.tokenize_paragraph_flat(raw_question) documents = [[tokenizer.tokenize_paragraph(p) for p in doc] for doc in documents] splitter = MergeParagraphs(400) documents = [splitter.split(doc) for doc in documents] if len(documents) == 1: selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5) context = selector.prune(question, documents[0]) else: selector = ShallowOpenWebRanker(n_to_select=10) context = selector.prune(question, flatten_iterable(documents)) context = [flatten_iterable(x.text) for x in context] data = [ ParagraphAndQuestion(x, question, None, "user-question%d" % i) for i, x in enumerate(context) ] encoded = model.encode(data, is_train=False) with sess.as_default(): spans, confid = sess.run([best_spans, conf], feed_dict=encoded) best_para = np.argmax(confid) ans = " ".join(context[best_para][spans[best_para][0]:spans[best_para][1] + 1]) confidence = confid[best_para] return ans, confidence
def read_input_data(model): data = [] vocab = set() tokenizer = NltkAndPunctTokenizer() with open(OPTS.input_file) as f: json_data = json.load(f) for doc in json_data['data']: for paragraph in doc['paragraphs']: context = tokenizer.tokenize_with_inverse(paragraph['context']) if model.preprocessor is not None: context = model.preprocessor.encode_text(question, context) context = context.get_context() vocab.update(context) for qa in paragraph['qas']: question = tokenizer.tokenize_sentence(qa['question']) vocab.update(question) ex = [ParagraphAndQuestion(context, question, None, qa['id'])] data.append((paragraph['context'], context, ex)) return data, sorted(list(vocab))
async def answer_question_spans( self, question: str ) -> Tuple[np.ndarray, np.ndarray, List[WebParagraph]]: """ Answer a question using web search, return the top spans and confidence scores for each paragraph """ paragraphs = await self.get_question_context(question) question = self.tokenizer.tokenize_paragraph_flat(question) paragraphs = self._preprocess(paragraphs) t0 = time.perf_counter() qa_pairs = [ ParagraphAndQuestion(c.get_context(), question, None, "") for c in paragraphs ] encoded = self.model.encode(qa_pairs, False) spans, scores = self.sess.run([self.span, self.score], encoded) self.log.info("Computing answer spans took %.5f seconds" % (time.perf_counter() - t0)) return spans, scores, paragraphs
def _build_expanded_batches(self, questions): out = [] for i, q in enumerate(questions): order = self._order[i] out.append(ParagraphSelection(q, order[self._on[i]])) self._on[i] += 1 if self._on[i] == len(order): self._on[i] = 0 np.random.shuffle(order) out.sort(key=lambda x: x.n_context_words) group = 0 for selection_batch in self.batcher.get_epoch(out): batch = [] for selected in selection_batch: q = selected.question if self.merge: paras = [q.paragraphs[i] for i in selected.selection] # Sort paragraph my reading order, not rank order paras.sort(key=lambda x: x.get_order()) answer_spans = [] text = [] for para in paras: answer_spans.append(len(text) + para.answer_spans) text += para.text batch.append( ParagraphAndQuestion( text, q.question, TokenSpans(q.answer_text, np.concatenate(answer_spans)), q.question_id)) else: for i in selected.selection: para = q.paragraphs[i] batch.append( para.build_qa_pair(q.question, q.question_id, q.answer_text, group)) group += 1 yield batch
def post_query(): document_raw = bottle.request.forms.getunicode('document').strip() question_raw = bottle.request.forms.getunicode('question').strip() document = re.split("\s*\n\s*", document_raw) question = tokenizer.tokenize_paragraph_flat(question_raw) doc_toks = [tokenizer.tokenize_paragraph(p) for p in document] split_doc = splitter.split(doc_toks) context = selector.prune(question, split_doc) if model.preprocessor is not None: context = [ model.preprocessor.encode_text(question, x) for x in context ] else: context = [flatten_iterable(x.text) for x in context] vocab = set(question) for txt in context: vocab.update(txt) data = [ ParagraphAndQuestion(x, question, None, "user-question%d" % i) for i, x in enumerate(context) ] model.word_embed.update(loader, vocab) encoded = model.encode(data, is_train=False) start_logits, end_logits, none_logit = sess.run( [start_logits_tf, end_logits_tf, none_logit_tf], feed_dict=encoded) beam, p_na = logits_to_probs(document_raw, context[0], start_logits, end_logits, none_logit, beam_size=BEAM_SIZE) return bottle.template('results', document=document_raw, question=question_raw, beam=beam, p_na=p_na)
def getAnswer(self): #parser = argparse.ArgumentParser(description="Run an ELMo model on user input") #parser.add_argument("model", help="Model directory") #parser.add_argument("question", help="Question to answer") #parser.add_argument("documents", help="List of text documents to answer the question with", nargs='+') #args = parser.parse_args() #print("Preprocessing...") # Load the model model_dir = ModelDir(MODEL_DIR) model = model_dir.get_model() if not isinstance(model, ParagraphQuestionModel): raise ValueError( "This script is built to work for ParagraphQuestionModel models only" ) conn = pyodbc.connect(DB_CONN) cursor = conn.cursor() #(23211,28690,33214,25638,25837,26454,28693,26137,31428,32087) query="select cast(filetext as varchar(max)) as filetext, name, type from dbo.UserworkspaceData where objectmasterid= "+\ str(self.ObjectMasterId)+\ " order by id asc" #query="select cast(filetext as varchar(max)) as filetext from kpl_tmp" documents = [] document = "" name = "" filetype = 0 for doc in cursor.execute(query): document = document + doc[0] name = doc[1] filetype = doc[2] #open("E:/kpl.txt","w+").write(document) documents.append(document) #documents.replace("\n\n","\n") #r.sub("",documents) #documents=" ".join(documents.split()) #open("E:\kpl_test.txt","w+").write(document) #doc="D:\Document QnA\document-qa-master\Data\Drug_Delivery_Surveying_Global_Competitive_Landscape_BMI.txt" # ============================================================================= # if not isfile(doc): # raise ValueError(doc + " does not exist") # with open(doc, "r") as f: # documents.append(f.read()) # ============================================================================= #print("Loaded %d documents" % len(documents)) #temp=documents[0].split() # Split documents into lists of paragraphs #documents=[" ".join(temp[i:(i+400)]) for i in range(1,len(temp),400)] documents = [re.split("\s*\n\s*", doc) for doc in documents] # Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer` # Note the model expects case-sensitive input tokenizer = NltkAndPunctTokenizer() question = tokenizer.tokenize_paragraph_flat( self.Question) # List of words # Now list of document->paragraph->sentence->word documents = [[tokenizer.tokenize_paragraph(p) for p in doc] for doc in documents] # Now group the document into paragraphs, this returns `ExtractedParagraph` objects # that additionally remember the start/end token of the paragraph within the source document splitter = MergeParagraphs(400) #splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping documents = [splitter.split(doc) for doc in documents] #print(str(len(documents))+" kpl") #kpl # Now select the top paragraphs using a `ParagraphFilter` if len(documents) == 1: # Use TF-IDF to select top paragraphs from the document selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5) context = selector.prune(question, documents[0]) else: # Use a linear classifier to select top paragraphs among all the documents selector = ShallowOpenWebRanker(n_to_select=10) context = selector.prune(question, flatten_iterable(documents)) #print("Select %d paragraph" % len(context)) if model.preprocessor is not None: # Models are allowed to define an additional pre-processing step # This will turn the `ExtractedParagraph` objects back into simple lists of tokens context = [ model.preprocessor.encode_text(question, x) for x in context ] else: # Otherwise just use flattened text context = [flatten_iterable(x.text) for x in context] #x=open("E:\context.txt","a+") #[x.write(" ".join(cont)) for cont in context] #x.write("\n.......................................................\n") #print("Setting up model") # Tell the model the batch size (can be None) and vocab to expect, This will load the # needed word vectors and fix the batch size to use when building the graph / encoding the input voc = set(question) for txt in context: voc.update(txt) model.set_input_spec(self.nlp, ParagraphAndQuestionSpec(batch_size=len(context)), voc) # Now we build the actual tensorflow graph, `best_span` and `conf` are # tensors holding the predicted span (inclusive) and confidence scores for each # element in the input batch, confidence scores being the pre-softmax logit for the span #print("Build tf graph") #kpl sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # We need to use sess.as_default when working with the cuNND stuff, since we need an active # session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this. with sess.as_default(): # 8 means to limit the span to size 8 or less best_spans, conf = model.get_prediction().get_best_span(8) # Loads the saved weights model_dir.restore_checkpoint(sess) # Now the model is ready to run # The model takes input in the form of `ContextAndQuestion` objects, for example: data = [ ParagraphAndQuestion(x, question, None, "user-question%d" % i) for i, x in enumerate(context) ] #print("Starting run") # The model is run in two steps, first it "encodes" a batch of paragraph/context pairs # into numpy arrays, then we use `sess` to run the actual model get the predictions encoded = model.encode( data, is_train=True) # batch of `ContextAndQuestion` -> feed_dict best_spans, conf = sess.run( [best_spans, conf], feed_dict=encoded) # feed_dict -> predictions best_para = np.argmax( conf ) # We get output for each paragraph, select the most-confident one to print #print("Best Paragraph: " + str(best_para)) #print("Best span: " + str(best_spans[best_para])) #print("Answer text: " + " ".join(context[best_para][best_spans[best_para][0]:best_spans[best_para][1]+1])) #print("Confidence: " + str(conf[best_para])) Answer = " ".join(context[best_para] [best_spans[best_para][0]:best_spans[best_para][1] + 1]) print("Confidence: " + str(conf[best_para])) print("Best Paragraph: " + str(best_para)) print("Best span: " + str(best_spans[best_para])) print("Answer text: " + Answer) print(" ".join(context[best_para])) context[best_para][best_spans[best_para][ 0]] = r"<em>" + context[best_para][best_spans[best_para][0]] context[best_para][best_spans[best_para][1]] = context[best_para][ best_spans[best_para][1]] + r"</em>" start = 0 end = len(context[best_para]) positions = [ x for x, n in enumerate(context[best_para] [0:best_spans[best_para][0]]) if n == "." ] if len(positions) >= 2: start = positions[len(positions) - 2] + 1 positions = [ x for x, n in enumerate(context[best_para][best_spans[best_para][1] + 1:]) if n == "." ] if len(positions) > 1: end = best_spans[best_para][1] + 1 + positions[1] d = dict() if conf[best_para] > 10: d["answer"] = Answer else: d["answer"] = "" d["name"] = name d["filetype"] = filetype d["paragraph"] = re.sub(r' (?=\W)', '', " ".join(context[best_para][start:end])) d["ObjectMasterId"] = self.ObjectMasterId return d #if __name__ == "__main__": # main()
def main(): parser = argparse.ArgumentParser( description="Run an ELMo model on user input") parser.add_argument("model", help="Model directory") parser.add_argument("question", help="Question to answer") parser.add_argument("context", help="Context to answer the question with") args = parser.parse_args() # Tokenize the input, the models expected data to be tokenized using `NltkAndPunctTokenizer` # Note the model expects case-sensitive input tokenizer = NltkAndPunctTokenizer() question = tokenizer.tokenize_paragraph_flat(args.question) context = tokenizer.tokenize_paragraph_flat(args.context) print("Loading model") model_dir = ModelDir(args.model) model = model_dir.get_model() if not isinstance(model, ElmoQaModel): raise ValueError( "This script is build to work for ElmoQaModel models only") # Important! This tells the language model not to use the pre-computed word vectors, # which are only applicable for the SQuAD dev/train sets. # Instead the language model will use its character-level CNN to compute # the word vectors dynamically. model.lm_model.embed_weights_file = None # Tell the model the batch size and vocab to expect, This will load the needed # word vectors and fix the batch size when building the graph / encoding the input print("Setting up model") voc = set(question) voc.update(context) model.set_input_spec(ParagraphAndQuestionSpec(batch_size=1), voc) # Now we build the actual tensorflow graph, `best_span` and `conf` are # tensors holding the predicted span (inclusive) and confidence scores for each # element in the input batch print("Build tf graph") sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) with sess.as_default(): # 17 means to limit the span to size 17 or less best_spans, conf = model.get_prediction().get_best_span(17) # Now restore the weights, this is a bit fiddly since we need to avoid restoring the # bilm weights, and instead load them from the pre-computed data all_vars = tf.global_variables() + tf.get_collection( tf.GraphKeys.SAVEABLE_OBJECTS) lm_var_names = {x.name for x in all_vars if x.name.startswith("bilm")} vars = [x for x in all_vars if x.name not in lm_var_names] model_dir.restore_checkpoint(sess, vars) # Run the initializer of the lm weights, which will load them from the lm directory sess.run( tf.variables_initializer( [x for x in all_vars if x.name in lm_var_names])) # Now the model is ready to run # The model takes input in the form of `ContextAndQuestion` objects, for example: data = [ParagraphAndQuestion(context, question, None, "user-question1")] print("Starting run") # The model is run in two steps, first it "encodes" the paragraph/context pairs # into numpy arrays, then to use `sess` to run the actual model get the predictions encoded = model.encode( data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict best_spans, conf = sess.run([best_spans, conf], feed_dict=encoded) # feed_dict -> predictions print("Best span: " + str(best_spans[0])) print("Answer text: " + " ".join(context[best_spans[0][0]:best_spans[0][1] + 1])) print("Confidence: " + str(conf[0]))
def get_test_questions(): paragraph = ["Harry", "Potter", "was", "written", "by", "JK"] question = ["Who", "wrote", "Harry", "Potter", "?"] return ParagraphAndQuestion(paragraph, question, None, "test_questions")
def main(Data: pd.DataFrame, nlp, model_dir, model): #parser = argparse.ArgumentParser(description="Run an ELMo model on user input") #parser.add_argument("model", help="Model directory") #parser.add_argument("question", help="Question to answer") #parser.add_argument("documents", help="List of text documents to answer the question with", nargs='+') #args = parser.parse_args() #print("Preprocessing...") # Load the model #model_dir = ModelDir(MODEL_DIR) #model = model_dir.get_model() print(model) if not isinstance(model, ParagraphQuestionModel): raise ValueError( "This script is built to work for ParagraphQuestionModel models only" ) #print(model) # Read the documents documents = [] documents.append(Data.at[0, 'Filetext']) """import pyodbc conn = pyodbc.connect("Driver={ODBC Driver 13 for SQL Server};" "Server=192.168.100.15;" "Database=PharmaAce;" "UID=sa;" "PWD=admin@123;" "Trusted_Connection=no;") cursor=conn.cursor() #(23211,28690,33214,25638,25837,26454,28693,26137,31428,32087) for doc in cursor.execute("select cast(filetext as varchar(max)) as filetext from kpl_tmp"): documents.append(doc[0]) #doc="D:\Document QnA\document-qa-master\Data\Drug_Delivery_Surveying_Global_Competitive_Landscape_BMI.txt" if not isfile(doc): raise ValueError(doc + " does not exist") with open(doc, "r") as f: documents.append(f.read()) """ #print("Loaded %d documents" % len(documents)) #temp=documents[0].split() # Split documents into lists of paragraphs #documents=[" ".join(temp[i:(i+400)]) for i in range(1,len(temp),400)] documents = [re.split("\s*\n\s*", doc) for doc in documents] # Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer` # Note the model expects case-sensitive input tokenizer = NltkAndPunctTokenizer() question = tokenizer.tokenize_paragraph_flat( Data.at[0, 'Question']) # List of words # Now list of document->paragraph->sentence->word documents = [[tokenizer.tokenize_paragraph(p) for p in doc] for doc in documents] # Now group the document into paragraphs, this returns `ExtractedParagraph` objects # that additionally remember the start/end token of the paragraph within the source document splitter = MergeParagraphs(400) #splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping documents = [splitter.split(doc) for doc in documents] #print(str(len(documents))+" kpl") #kpl # Now select the top paragraphs using a `ParagraphFilter` print(len(documents)) #kpl if len(documents) == 1: # Use TF-IDF to select top paragraphs from the document selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5) context = selector.prune(question, documents[0]) else: # Use a linear classifier to select top paragraphs among all the documents selector = ShallowOpenWebRanker(n_to_select=10) context = selector.prune(question, flatten_iterable(documents)) #print("Select %d paragraph" % len(context)) if model.preprocessor is not None: # Models are allowed to define an additional pre-processing step # This will turn the `ExtractedParagraph` objects back into simple lists of tokens context = [ model.preprocessor.encode_text(question, x) for x in context ] else: # Otherwise just use flattened text context = [flatten_iterable(x.text) for x in context] print("Setting up model") # Tell the model the batch size (can be None) and vocab to expect, This will load the # needed word vectors and fix the batch size to use when building the graph / encoding the input voc = set(question) for txt in context: voc.update(txt) model.set_input_spec(nlp, ParagraphAndQuestionSpec(batch_size=len(context)), voc) # Now we build the actual tensorflow graph, `best_span` and `conf` are # tensors holding the predicted span (inclusive) and confidence scores for each # element in the input batch, confidence scores being the pre-softmax logit for the span #print("Build tf graph") #kpl print("after set input spec") sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # We need to use sess.as_default when working with the cuNND stuff, since we need an active # session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this. with sess.as_default(): # 8 means to limit the span to size 8 or less best_spans, conf = model.get_prediction().get_best_span(8) # Loads the saved weights model_dir.restore_checkpoint(sess) print("after loading weights") # Now the model is ready to run # The model takes input in the form of `ContextAndQuestion` objects, for example: data = [ ParagraphAndQuestion(x, question, None, "user-question%d" % i) for i, x in enumerate(context) ] #print("Starting run") # The model is run in two steps, first it "encodes" a batch of paragraph/context pairs # into numpy arrays, then we use `sess` to run the actual model get the predictions encoded = model.encode( data, is_train=True) # batch of `ContextAndQuestion` -> feed_dict best_spans, conf = sess.run([best_spans, conf], feed_dict=encoded) # feed_dict -> predictions best_para = np.argmax( conf ) # We get output for each paragraph, select the most-confident one to print #print("Best Paragraph: " + str(best_para)) #print("Best span: " + str(best_spans[best_para])) #print("Answer text: " + " ".join(context[best_para][best_spans[best_para][0]:best_spans[best_para][1]+1])) #print("Confidence: " + str(conf[best_para])) return " ".join( context[best_para][best_spans[best_para][0]:best_spans[best_para][1] + 1]) #if __name__ == "__main__": # main()
def main(): parser = argparse.ArgumentParser( description="Run an ELMo model on user input") parser.add_argument("model", help="Model directory") parser.add_argument("ja_filepath", help="File path to japanese questions") parser.add_argument("result_file", help="File path to predicted result json") args = parser.parse_args() print(args) print("Preprocessing...") paragraphs, questions = read_squad_style_database(args.ja_filepath) # Load the model model_dir = ModelDir(args.model) model = model_dir.get_model() if not isinstance(model, ParagraphQuestionModel): raise ValueError( "This script is built to work for ParagraphQuestionModel models only" ) paragraphs, questions = read_squad_style_database(args.ja_filepath) predictions = {} predictions["conf"] = {} for qa in questions: print(qa["id"]) title = qa["title"] para_idx = qa["para_idx"] context = paragraphs[title][para_idx] question = qa["question"] print(context) print(question) if model.preprocessor is not None: context = [ model.preprocessor.encode_text(question, x) for x in context ] print("Setting up model") voc = set(question) for txt in context: voc.update(txt) model.set_input_spec(ParagraphAndQuestionSpec(batch_size=len(context)), voc) print("Build tf graph") sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) with sess.as_default(): best_spans, conf = model.get_prediction().get_best_span(8) # Loads the saved weights model_dir.restore_checkpoint(sess) data = [ ParagraphAndQuestion(x, question, None, "user-question%d" % i) for i, x in enumerate(context) ] print("Starting run") encoded = model.encode( data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict best_spans, conf = sess.run( [best_spans, conf], feed_dict=encoded) # feed_dict -> predictions print(best_spans) predictions[qa["id"]] = best_spans predictions["conf"][qa["id"]] = conf print(predictions) result_f = open(args.result_file, "w") json.dump(predictions, result_f) exit() official_evaluator = OfficialEvaluator(args.ja_filepath, args.result_file) evaluation = official_evaluator.evaluate() print(evaluation)
def main(): parser = argparse.ArgumentParser(description="Run an ELMo model on user input") # parser.add_argument("model", type=int, help="Model directory") parser.add_argument("question", help="Question to answer") parser.add_argument("documents", help="List of text documents to answer the question with", nargs='+') args = parser.parse_args() # Models path SQUAD_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/squad' SQUAD_SHARED_NORM_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/squad-shared-norm' TRIVIAQA_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/triviaqa-unfiltered-shared-norm' TRIVIAQA_SHARED_NORM_MODEL_DIRECTORY_PATH = 'docqa/models-cpu/triviaqa-web-shared-norm' models_directory = [ SQUAD_MODEL_DIRECTORY_PATH, SQUAD_SHARED_NORM_MODEL_DIRECTORY_PATH, TRIVIAQA_MODEL_DIRECTORY_PATH, TRIVIAQA_SHARED_NORM_MODEL_DIRECTORY_PATH ] print("Preprocessing...") # Load the model # model_dir = ModelDir(args.model) model_dir = ModelDir(models_directory[0]) model = model_dir.get_model() if not isinstance(model, ParagraphQuestionModel): raise ValueError("This script is built to work for ParagraphQuestionModel models only") # Read the documents documents = [] for doc in args.documents: if not isfile(doc): raise ValueError(doc + " does not exist") with open(doc, "r") as f: documents.append(f.read()) print("Loaded %d documents" % len(documents)) # Split documents into lists of paragraphs documents = [re.split("\s*\n\s*", doc) for doc in documents] # Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer` # Note the model expects case-sensitive input tokenizer = NltkAndPunctTokenizer() question = tokenizer.tokenize_paragraph_flat(args.question) # List of words # Now list of document->paragraph->sentence->word documents = [[tokenizer.tokenize_paragraph(p) for p in doc] for doc in documents] # Now group the document into paragraphs, this returns `ExtractedParagraph` objects # that additionally remember the start/end token of the paragraph within the source document splitter = MergeParagraphs(400) # splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping documents = [splitter.split(doc) for doc in documents] # Now select the top paragraphs using a `ParagraphFilter` if len(documents) == 1: # Use TF-IDF to select top paragraphs from the document selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=5) context = selector.prune(question, documents[0]) else: # Use a linear classifier to select top paragraphs among all the documents selector = ShallowOpenWebRanker(n_to_select=10) context = selector.prune(question, flatten_iterable(documents)) print("Select %d paragraph" % len(context)) if model.preprocessor is not None: # Models are allowed to define an additional pre-processing step # This will turn the `ExtractedParagraph` objects back into simple lists of tokens context = [model.preprocessor.encode_text(question, x) for x in context] else: # Otherwise just use flattened text context = [flatten_iterable(x.text) for x in context] print("Setting up model") # Tell the model the batch size (can be None) and vocab to expect, This will load the # needed word vectors and fix the batch size to use when building the graph / encoding the input voc = set(question) for txt in context: voc.update(txt) model.set_input_spec(ParagraphAndQuestionSpec(batch_size=len(context)), voc) # Now we build the actual tensorflow graph, `best_span` and `conf` are # tensors holding the predicted span (inclusive) and confidence scores for each # element in the input batch, confidence scores being the pre-softmax logit for the span print("Build tf graph") sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # We need to use sess.as_default when working with the cuNND stuff, since we need an active # session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this. with sess.as_default(): # 8 means to limit the span to size 8 or less best_spans, conf = model.get_prediction().get_best_span(10) # Loads the saved weights model_dir.restore_checkpoint(sess) # Now the model is ready to run # The model takes input in the form of `ContextAndQuestion` objects, for example: data = [ParagraphAndQuestion(x, question, None, "user-question%d"%i) for i, x in enumerate(context)] print("Starting run") # The model is run in two steps, first it "encodes" a batch of paragraph/context pairs # into numpy arrays, then we use `sess` to run the actual model get the predictions encoded = model.encode(data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict best_spans, conf = sess.run([best_spans, conf], feed_dict=encoded) # feed_dict -> predictions best_para = np.argmax(conf) # We get output for each paragraph, select the most-confident one to print print("Best Paragraph: " + str(best_para)) para_id = int(str(best_para)) # print("Best Paragraph: \n" + (" ".join((paras[para_id].text)[0]))) print("Best Paragraph: \n" + " ".join(context[para_id])) print("Best span: " + str(best_spans[best_para])) print("Answer text: " + " ".join(context[best_para][best_spans[best_para][0]:best_spans[best_para][1]+1])) print("Confidence: " + str(conf[best_para]))
def predict(): json_data = {"success": False, "predictions": []} print("Preprocessing...") # Load the model model_dir = ModelDir( "/home/antriv/conversation_ai/Transfer_Learning/ALLENAI_DocumentQA/document-qa/pretrained_models/models/triviaqa-unfiltered-shared-norm" ) model = model_dir.get_model() if not isinstance(model, ParagraphQuestionModel): raise ValueError( "This script is built to work for ParagraphQuestionModel models only" ) # Load the question question = (flask.request.data).decode("utf-8") # Read the documents documents = [] doclist = ["/home/antriv/data/The-Future-Computed.txt"] for doc in doclist: if not isfile(doc): raise ValueError(doc + " does not exist") with open(doc, "r") as f: documents.append(f.read()) print("Loaded %d documents" % len(documents)) # Split documents into lists of paragraphs documents = [re.split("\s*\n\s*", doc) for doc in documents] # Tokenize the input, the models expects data to be tokenized using `NltkAndPunctTokenizer` # Note the model expects case-sensitive input tokenizer = NltkAndPunctTokenizer() question = tokenizer.tokenize_paragraph_flat(question) # List of words # Now list of document->paragraph->sentence->word documents = [[tokenizer.tokenize_paragraph(p) for p in doc] for doc in documents] # Now group the document into paragraphs, this returns `ExtractedParagraph` objects # that additionally remember the start/end token of the paragraph within the source document splitter = MergeParagraphs(400) #splitter = PreserveParagraphs() # Uncomment to use the natural paragraph grouping documents = [splitter.split(doc) for doc in documents] # Now select the top paragraphs using a `ParagraphFilter` if len(documents) == 1: # Use TF-IDF to select top paragraphs from the document selector = TopTfIdf(NltkPlusStopWords(True), n_to_select=1000) context = selector.prune(question, documents[0]) else: # Use a linear classifier to select top paragraphs among all the documents selector = ShallowOpenWebRanker(n_to_select=1000) context = selector.prune(question, flatten_iterable(documents)) print("Select %d paragraph" % len(context)) if model.preprocessor is not None: # Models are allowed to define an additional pre-processing step # This will turn the `ExtractedParagraph` objects back into simple lists of tokens context = [ model.preprocessor.encode_text(question, x) for x in context ] else: # Otherwise just use flattened text context = [flatten_iterable(x.text) for x in context] print("Setting up model") # Tell the model the batch size (can be None) and vocab to expect, This will load the # needed word vectors and fix the batch size to use when building the graph / encoding the input voc = set(question) for txt in context: voc.update(txt) model.set_input_spec(ParagraphAndQuestionSpec(batch_size=len(context)), voc) # Now we build the actual tensorflow graph, `best_span` and `conf` are # tensors holding the predicted span (inclusive) and confidence scores for each # element in the input batch, confidence scores being the pre-softmax logit for the span print("Build tf graph") sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # We need to use sess.as_default when working with the cuNND stuff, since we need an active # session to figure out the # of parameters needed for each layer. The cpu-compatible models don't need this. with sess.as_default(): # 8 means to limit the span to size 8 or less best_spans, conf = model.get_prediction().get_best_span(8) # Loads the saved weights model_dir.restore_checkpoint(sess) # Now the model is ready to run # The model takes input in the form of `ContextAndQuestion` objects, for example: data = [ ParagraphAndQuestion(x, question, None, "user-question%d" % i) for i, x in enumerate(context) ] print("Starting run") # The model is run in two steps, first it "encodes" a batch of paragraph/context pairs # into numpy arrays, then we use `sess` to run the actual model get the predictions encoded = model.encode( data, is_train=False) # batch of `ContextAndQuestion` -> feed_dict best_spans, conf = sess.run([best_spans, conf], feed_dict=encoded) # feed_dict -> predictions best_para = np.argmax( conf ) # We get output for each paragraph, select the most-confident one to print print("Best Paragraph: " + str(best_para)) print("Best span: " + str(best_spans[best_para])) print("Answer text: " + " ".join(context[best_para] [best_spans[best_para][0]:best_spans[best_para][1] + 1])) print("Confidence: " + str(conf[best_para])) y_output = " ".join( context[best_para][best_spans[best_para][0]:best_spans[best_para][1] + 1]) print(y_output) json_data["predictions"].append(str(y_output)) #indicate that the request was a success json_data["success"] = True #return the data dictionary as a JSON response return flask.jsonify(json_data)
def convert(model_dir, output_dir, best_weights=False): print("Load model") md = ModelDir(model_dir) model = md.get_model() dim = model.embed_mapper.layers[1].n_units global_step = tf.get_variable('global_step', shape=[], dtype='int32', initializer=tf.constant_initializer(0), trainable=False) print("Setting up cudnn version") #global_step = tf.get_variable('global_step', shape=[], dtype='int32', trainable=False) sess = tf.Session() sess.run(global_step.assign(0)) with sess.as_default(): model.set_input_spec( ParagraphAndQuestionSpec(1, None, None, 14), {"the"}, ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)})) print("Buiding graph") pred = model.get_prediction() test_questions = ParagraphAndQuestion( ["Harry", "Potter", "was", "written", "by", "JK"], ["Who", "wrote", "Harry", "Potter", "?"], None, "test_questions") print("Load vars") md.restore_checkpoint(sess) print("Restore finished") feed = model.encode([test_questions], False) cuddn_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed) print("Done, copying files...") if not exists(output_dir): mkdir(output_dir) for file in listdir(model_dir): if isfile(file) and file != "model.npy": copyfile(join(model_dir, file), join(output_dir, file)) print("Done, mapping tensors...") to_save = [] to_init = [] for x in tf.trainable_variables(): if x.name.endswith("/gru_parameters:0"): key = x.name[:-len("/gru_parameters:0")] fw_params = x if "map_embed" in x.name: c = cudnn_rnn_ops.CudnnGRU(1, dim, 400) elif "chained-out" in x.name: c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 4) else: c = cudnn_rnn_ops.CudnnGRU(1, dim, dim * 2) params_saveable = cudnn_rnn_ops.RNNParamsSaveable( c, c.params_to_canonical, c.canonical_to_params, [fw_params], key) for spec in params_saveable.specs: if spec.name.endswith("bias_cudnn 0") or \ spec.name.endswith("bias_cudnn 1"): # ??? What do these even do? continue name = spec.name.split("/") name.remove("cell_0") if "forward" in name: ix = name.index("forward") name.insert(ix + 2, "fw") else: ix = name.index("backward") name.insert(ix + 2, "bw") del name[ix] ix = name.index("multi_rnn_cell") name[ix] = "bidirectional_rnn" name = "/".join(name) v = tf.Variable(sess.run(spec.tensor), name=name) to_init.append(v) to_save.append(v) else: to_save.append(x) other = [ x for x in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) if x not in tf.trainable_variables() ] print(other) sess.run(tf.initialize_variables(to_init)) saver = tf.train.Saver(to_save + other) save_dir = join(output_dir, "save") if not exists(save_dir): mkdir(save_dir) saver.save(sess, join(save_dir, "checkpoint"), sess.run(global_step)) sess.close() tf.reset_default_graph() print("Updating model...") model.embed_mapper.layers = [ model.embed_mapper.layers[0], BiRecurrentMapper(CompatGruCellSpec(dim)) ] model.match_encoder.layers = list(model.match_encoder.layers) other = model.match_encoder.layers[1].other other.layers = list(other.layers) other.layers[1] = BiRecurrentMapper(CompatGruCellSpec(dim)) pred = model.predictor.predictor pred.first_layer = BiRecurrentMapper(CompatGruCellSpec(dim)) pred.second_layer = BiRecurrentMapper(CompatGruCellSpec(dim)) with open(join(output_dir, "model.pkl"), "wb") as f: pickle.dump(model, f) print("Testing...") with open(join(output_dir, "model.pkl"), "rb") as f: model = pickle.load(f) sess = tf.Session() model.set_input_spec( ParagraphAndQuestionSpec(1, None, None, 14), {"the"}, ResourceLoader(lambda a, b: {"the": np.zeros(300, np.float32)})) pred = model.get_prediction() print("Rebuilding") saver = tf.train.Saver() saver.restore(sess, tf.train.latest_checkpoint(save_dir)) feed = model.encode([test_questions], False) cpu_out = sess.run([pred.start_logits, pred.end_logits], feed_dict=feed) print("These should be close:") print([np.allclose(a, b) for a, b in zip(cpu_out, cuddn_out)]) print(cpu_out) print(cuddn_out)