Kurt Shuster

Q1: You've specified the incorrect `--init-model` --> try doing the following: ``` --init-model zoo:pretrained_transformers/bi_model_huge_reddit/model -m transformer/ranker ``` q2: I'm not sure I fully understand - BB2 is a generative model,...

We use a DPR model that is pre-trained on a suite of knowledge-intensive QA tasks, and is fine-tuned in a RAG setup on wizard of wikipedia. so, no reddit training...

BlenderBot 1 is an encoder-decoder transformer, not a poly-encoder BlenderBot 2's long term memory decoder is also BART and was trained on MSC. everything else looks to be correct

BlenderBot 1 does not use BART, it uses a different architecture with a different pre-training objective; the original paper outlines in depth what these are. it's simply a transformer seq2seq...

That is the retrieve and refine architecture; blenderbot 1 is a purely generative model, however we considered retrieve and refine in the original tech report as an alternative

bb1 was pre-trained on reddit as well, as a generative language model

``` self = def test_base_task(self): with testing_utils.tempdir() as tmpdir: # Paths expected_states_folder = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'expected_states' ) expected_chat_data_path = os.path.join( expected_states_folder, 'final_chat_data.json' ) expected_state_path = os.path.join(expected_states_folder, 'state.json') model_opt_path = os.path.join(tmpdir,...

``` self = def test_mp_eval(self): args = dict( task='integration_tests:multiturn_nocandidate', model='seq2seq', model_file='zoo:unittest/seq2seq/model', dict_file='zoo:unittest/seq2seq/model.dict', skip_generation=False, batchsize=8, ) valid, _ = testing_utils.eval_model(args, skip_test=True) from parlai.scripts.multiprocessing_eval import MultiProcessEval > valid_mp = MultiProcessEval.main(**args) tests/test_distributed.py:218: _...

``` self = @contextmanager def _error_catcher(self): """ Catch low-level python exceptions, instead re-raising urllib3 variants, so that low-level exceptions are not leaked in the high-level api. On exit, release the...

``` self = @testing_utils.retry(ntries=3) def test_train_batch_all(self): args = self._get_args() args['candidates'] = 'batch-all-cands' args['eval_candidates'] = 'batch-all-cands' valid, test = testing_utils.train_model(args) threshold = self._get_threshold() > self.assertGreaterEqual(valid['hits@1'], threshold) E AssertionError: AverageMetric(0.5) not greater...