diff --git a/inference/db_state/state.csv b/inference/db_state/state.csv index 9264e19..f805925 100644 --- a/inference/db_state/state.csv +++ b/inference/db_state/state.csv @@ -55,7 +55,7 @@ incident_id,count,mean 54,19,"[-0.0570942647755146, 0.05686115473508835, 0.022522253915667534, -0.09824235737323761, 0.10484474897384644, -0.0317285880446434, -0.006028921343386173, 0.06214400380849838, 0.08043604344129562, -0.1343563199043274, -0.00908948015421629, 0.0816216990351677, 0.017512518912553787, -0.072564497590065, 0.019834840670228004, -0.07994207739830017, -0.11187782138586044, -0.063690185546875, -0.03379661217331886, -0.12725946307182312, -0.05675145983695984, -0.012270672246813774, -0.013949858956038952, 0.10736344754695892, -0.034582726657390594, 0.04143638536334038, 0.08325620740652084, 0.11108150333166122, -0.07162725925445557, 0.049341168254613876, -0.01584744080901146, -0.08786317706108093, 0.12994885444641113, 0.0007402093033306301, -0.01672983728349209, 0.10642796009778976, 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-0.07650570571422577, 0.03746692091226578, 0.06348885595798492, 0.06304892897605896, -0.11870436370372772, 0.02188977785408497, -0.058229975402355194]" diff --git a/inference/similar.py b/inference/similar.py index 992c58c..d479c5d 100644 --- a/inference/similar.py +++ b/inference/similar.py @@ -42,20 +42,21 @@ def list_files(startpath): # model = LongformerModel.from_pretrained(model_dir, local_files_only=True) # # Constants -# incidents_path = os.path.join( +# STATE_DOC = os.path.join( # os.environ["TRANSFORMERS_CACHE"], os.environ["INCIDENTS_FILENAME"]) # csv_path = os.path.join( # os.environ["TRANSFORMERS_CACHE"], os.environ["CSV_FILENAME"]) -model = LongformerModel.from_pretrained( - '/function/model', local_files_only=True) -tokenizer = LongformerTokenizer.from_pretrained( - '/function/model', local_files_only=True) -csv_path = '/function/db_state/incidents.csv' -incidents_path = '/function/db_state/state.csv' -best_of_def = 3 + +STATE_DOC = '/function/db_state/state.csv' +BEST_OF = 3 +tokenizer = LongformerTokenizer.from_pretrained('/function/model', + local_files_only=True, + model_max_length=4096) +model = LongformerModel.from_pretrained('/function/model', + local_files_only=True) # Load in a list of incident states from a CSV -state = pd.read_csv(incidents_path, converters={"mean": literal_eval}) +state = pd.read_csv(STATE_DOC, converters={"mean": literal_eval}) def test(text): @@ -72,7 +73,7 @@ def test(text): return sims -def inputted(whole_text, best_of=best_of_def): +def inputted(whole_text, best_of=BEST_OF): sims = [j for j in sorted(test(whole_text), reverse=True)] if (best_of >= 0): return sims[:best_of] @@ -81,7 +82,7 @@ def inputted(whole_text, best_of=best_of_def): # What to do to correctly formatted input event_text -def process(event_text, best_of=best_of_def): +def process(event_text, best_of=BEST_OF): # return tokenizer(event_text) return inputted(event_text, best_of) @@ -112,7 +113,7 @@ def handler(event, context): # return result # Get "best of" value from body or query string (or <0 for full list) - best_of = best_of_def + best_of = BEST_OF if ('num' in event): best_of = event['num'] elif ('body' in event and event['body'] != '' and 'num' in json.loads(event['body'])): @@ -122,18 +123,18 @@ def handler(event, context): # Assign to best of if possible try: - if (best_of != best_of_def): # if input found (type/value mismatch) + if (best_of != BEST_OF): # if input found (type/value mismatch) best_of = int(best_of) except ValueError: - best_of = best_of_def + best_of = BEST_OF result['body']['warnings'].append( - f'Provided value for "num" invalid, using default of {best_of_def}.') + f'Provided value for "num" invalid, using default of {BEST_OF}.') if (best_of == 0): result['body']['warnings'].append( f'Zero results requested with the "num" value of 0. Use value <0 for maximum possible.') # Handle unicode in event_text - event_text = unidecode(event_text[:6000]) + event_text = unidecode(event_text) # Found event_text, use it and return result try: diff --git a/state_update.py b/state_update.py index a2fc0e7..cb2f048 100644 --- a/state_update.py +++ b/state_update.py @@ -16,7 +16,9 @@ MODEL_PATH = path.join('inference', 'model') # Get the Longformer tokenizer and model -tokenizer = LongformerTokenizer.from_pretrained(MODEL_PATH, local_files_only=True) +tokenizer = LongformerTokenizer.from_pretrained(MODEL_PATH, + local_files_only=True, + model_max_length=4096) model = LongformerModel.from_pretrained(MODEL_PATH, local_files_only=True) @@ -49,7 +51,7 @@ def cls_token(text): 'localField': 'reports', 'foreignField': 'report_number', 'pipeline': [ - {'$project': {'_id': False, 'text': True}} + {'$project': {'_id': False, 'text': True, 'report_number': True}} ], 'as': 'reports' }} @@ -58,7 +60,7 @@ def cls_token(text): # Update the state using the results for result in results: - # print('checking', result['incident_id']) + print('checking', result['incident_id']) # DEBUG # Process the first report and store a row for incidents not in the state if result['incident_id'] not in state.index: @@ -66,12 +68,12 @@ def cls_token(text): row = DataFrame({'count': [1], 'mean': [token.detach().tolist()]}, index=[result['incident_id']]) state = concat([state, row]) - # print('added incident') + print('added incident') # DEBUG # Process the text of new reports, and store new mean for each incident count = int(state.loc[result['incident_id'], 'count']) for report in result['reports'][count:]: - # print('adding report, count', count) + print('adding report', report['report_number'], 'count', count) # DEBUG token = cls_token(report['text']) mean = tensor(state.loc[result['incident_id'], 'mean']) new = token.add(mean, alpha=count).div(count + 1).detach().tolist()