def download(datapath): opt = {'datapath': datapath} opt['model'] = 'projects.personachat.kvmemnn.kvmemnn:Kvmemnn' opt['model_file'] = 'models:convai2/kvmemnn/model' opt['model_type'] = 'kvmemnn' # for builder fnames = ['kvmemnn.tgz'] download_models(opt, fnames, 'convai2')
def download(datapath): opt = { 'datapath': datapath, 'model_type': 'biranker_dialogue' } # for builder fnames = ['biranker_dialogue.tar.gz'] download_models(opt, fnames, 'light', version='v0.5', use_model_type=True)
def download(datapath): """ Download the model. """ opt = {'datapath': datapath, 'model_type': 'transresnet'} fnames = ['transresnet.tgz'] download_models(opt, fnames, 'personality_captions')
def download(datapath): opt = { 'datapath': datapath, 'model_type': 'seq2seq' } fnames = ['twitter_seq2seq_model.tgz'] download_models(opt, fnames, 'twitter', version='v1.0', use_model_type=True)
def download(datapath): """ Download the model. """ opt = {'datapath': datapath, 'model_type': 'transresnet_multimodal'} fnames = ['transresnet_multimodal.tgz'] download_models(opt, fnames, 'image_chat')
def download(datapath): opt = {'datapath': datapath} fnames = ['hh131k_hb60k_fb60k_st1k_v1.tar.gz'] download_models(opt, fnames, 'self_feeding', version='v1.0', use_model_type=False)
def download_with_model_type(datapath, model_type, version): ddir = os.path.join(get_model_dir(datapath), 'light_whoami') if not built(os.path.join(ddir, model_type), version): opt = {'datapath': datapath, 'model_type': model_type} fnames = ['model.tgz'] download_models( opt, fnames, 'light_whoami', version=version, use_model_type=True )
def download(datapath): opt = {'datapath': datapath} fnames = ['models_v1.tar.gz'] download_models(opt, fnames, 'controllable_dialogue', version='v1.0', use_model_type=False)
def download(datapath): ddir = os.path.join(get_model_dir(datapath), 'msc') model_type = 'summsc_rag3B' version = 'v0.1' if not built(os.path.join(ddir, model_type), version): opt = {'datapath': datapath, 'model_type': model_type} fnames = [f'model_{version}.tar.gz'] download_models(opt, fnames, 'msc', version=version, use_model_type=True)
def download(datapath): ddir = os.path.join(get_model_dir(datapath), 'sea') model_type = 'bart_sq_gen' version = 'v1.0' if not built(os.path.join(ddir, model_type), version): opt = {'datapath': datapath, 'model_type': model_type} fnames = [f'model_{version}.tgz'] download_models(opt, fnames, 'sea', version=version, use_model_type=True)
def download(datapath): opt = {'datapath': datapath} fnames = ['glove.840B.300d.zip'] download_models(opt, fnames, 'glove_vectors', use_model_type=False, path="http://nlp.stanford.edu/data")
def download(datapath): model_name = 'pretrained_transformers' mdir = os.path.join(get_model_dir(datapath), model_name) version = 'v3.0' if not built(mdir, version): opt = {'datapath': datapath} fnames = ['pretrained_transformers.tgz'] download_models(opt, fnames, model_name, version=version, use_model_type=False)
def download(datapath): opt = {'datapath': datapath} # for builder fnames = ['safety_models_v1.tgz'] download_models(opt, fnames, 'dialogue_safety', version='v0.5', use_model_type=False)
def download(datapath): opt = {'datapath': datapath} fnames = ['end2end_generator_0.tar.gz'] download_models(opt, fnames, 'wizard_of_wikipedia', version='v0.5', use_model_type=False)
def build(datapath, fname, model_type, version): opt = {'datapath': datapath} opt['model_type'] = model_type dpath = os.path.join(datapath, 'models', 'dialogue_unlikelihood', model_type) if not built(dpath, version): download_models( opt, [fname], 'dialogue_unlikelihood', version=version, use_model_type=False )
def download(datapath): opt = {'datapath': datapath} model_filenames = [ 'seq2seq.tar.gz', 'transformer_ranker.tar.gz', 'transformer_generator2.tar.gz', 'memnn.tar.gz', ] download_models(opt, model_filenames, 'unittest', version='v3.0')
def download_unittest_models(): from parlai.core.params import ParlaiParser from parlai.core.build_data import download_models opt = ParlaiParser().parse_args(print_args=False) model_filenames = [ 'seq2seq.tar.gz', 'transformer_ranker.tar.gz', 'transformer_generator2.tar.gz' ] with capture_output() as _: download_models(opt, model_filenames, 'unittest', version='v2.0')
def build(datapath, fname, model_type, version): opt = {'datapath': datapath} opt['model_type'] = model_type dpath = os.path.join(datapath, 'models', 'blender', model_type) if not built(dpath, version): print_blender() download_models(opt, [fname], 'blender', version=version, use_model_type=False)
def download(datapath): ddir = os.path.join(get_model_dir(datapath), 'blenderbot2') model_type = 'memory_decoder' version = 'v1.0' if not built(os.path.join(ddir, model_type), version): opt = {'datapath': datapath, 'model_type': model_type} fnames = ['model.tgz'] download_models( opt, fnames, 'blenderbot2', version=version, use_model_type=True )
def download(datapath): ddir = os.path.join(get_model_dir(datapath), 'tod_base_no_api') model_type = 'tod_base_no_api' version = 'v1.0' if not built(os.path.join(ddir, model_type), version): opt = {'datapath': datapath, 'model_type': model_type} fnames = ['model.tar.gz'] download_models( opt, fnames, 'tod', version=version, path='aws', use_model_type=True )
def download(datapath): version = 'v1.0' fnames = ['md_gender_classifier.tgz'] download_models( opt={'datapath': datapath}, fnames=fnames, model_folder='md_gender', version=version, use_model_type=False, )
def download(datapath): model_name = 'tutorial_transformer_generator' mdir = os.path.join(get_model_dir(datapath), model_name) version = 'v1' if not built(mdir, version): opt = {'datapath': datapath} fnames = ['tutorial_transformer_generator_v1.tar.gz'] download_models( opt, fnames, model_name, version=version, use_model_type=False, )
def download(datapath): ddir = os.path.join(get_model_dir(datapath), 'hallucination') model_type = 'bart_rag_dpr_poly' version = 'v1.0' if not built(os.path.join(ddir, model_type), version): opt = {'datapath': datapath, 'model_type': model_type} fnames = ['model.tgz'] download_models( opt, fnames, 'hallucination', version=version, use_model_type=True )
def download(datapath): ddir = os.path.join(get_model_dir(datapath), 'hallucination') version = 'v1.0' if not built(ddir, version): opt = {'datapath': datapath, 'model_type': 'wow_passages'} fnames = ['wow_articles.paragraphs.tgz', 'exact.tgz', 'compressed.tgz'] download_models(opt, fnames, 'hallucination', version=version, use_model_type=True)
def download(datapath): opt = {'datapath': datapath} # download all relevant wizard of wikipedia models generator_download(datapath) retrieval_download(datapath) # now download knowledge retriever fnames = ['knowledge_retriever.tgz'] opt['model_type'] = 'knowledge_retriever' download_models(opt, fnames, 'wizard_of_wikipedia', version='v3.0')
def download(datapath): opt = {'datapath': datapath} version = 'v0.1' fnames = [f'models_{version}.tar.gz'] download_models( opt, fnames, model_folder='saferdialogues', version=version, use_model_type=False, )
def download(datapath): version = 'v1' model_type = 'multi_turn' opt = {'datapath': datapath, 'model_type': model_type} fnames = [f'models_{version}.tar.gz'] download_models( opt=opt, fnames=fnames, model_folder='bot_adversarial_dialogue', version=version, use_model_type=True, )
def download(datapath): model_type = 'gender_ethnicity__name_scrambling' version = 'v1.0' opt = {'datapath': datapath, 'model_type': model_type} fnames = [f'{version}.tar.gz'] download_models( opt=opt, fnames=fnames, model_folder='dialogue_bias', version=version, use_model_type=True, )
def download(datapath): model_type = 'gender__unlikelihood_sequence_level' version = 'v1.0' opt = {'datapath': datapath, 'model_type': model_type} fnames = [f'{version}.tar.gz'] download_models( opt=opt, fnames=fnames, model_folder='dialogue_bias', version=version, use_model_type=True, )
def download(datapath): model_type = 'convai2_single_task' version = 'v1.0' opt = {'datapath': datapath, 'model_type': model_type} fnames = [f'{version}.tar.gz'] download_models( opt=opt, fnames=fnames, model_folder='blended_skill_talk', version=version, use_model_type=True, )