Delete ControllableComplexityChatbot-huggingface-compatible

ControllableComplexityChatbot-huggingface-compatible/.gitattributes

@@ -1 +0,0 @@
-complexity_model/pytorch_model.bin filter=lfs diff=lfs merge=lfs -text

ControllableComplexityChatbot-huggingface-compatible/.gitignore

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-.idea

ControllableComplexityChatbot-huggingface-compatible/README.md

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-# About
-This repository contains the code, models, and data accompanying our paper on adjusting text difficulty of messages generated by open-domain chatbots.
-
-Tyen, G., Brenchley, M., Caines, A., & Buttery, P. (2022). Towards an open-domain chatbot for language practice. 17th Workshop on Innovative Use of NLP for Building Educational Applications.
-
-# Dependencies
-* ParlAI 1.6.0
-* PyTorch 1.10.2
-* Huggingface Transformers 4.16.2
-* NumPy 1.22.2
-* SciPy 1.8.0
-* Regex 2022.1.18
-
-# To run the demo:
-1. Download and cd to project directory
-   `git clone https://github.com/WHGTyen/ControllableComplexityChatbot`
-   `cd ControllableComplexityChatbot`
-2. Install pip dependencies
-   `pip install numpy scipy regex torch transformers`
-3. Clone ParlAI repository
-   `git clone https://github.com/facebookresearch/ParlAI.git --branch 1.6.0`
-4. Setup ParlAI
-   `cd ParlAI; python setup.py develop; cd ..`
-5. To run the demo:
-   `python demo.py`
-6. To adjust generation parameters, edit values in `demo.py`
-   
-# Data
-* `sample_wordlist.txt` is the 5000 most frequent words from [this list](https://github.com/first20hours/google-10000-english/blob/d0736d492489198e4f9d650c7ab4143bc14c1e9e/google-10000-english-no-swears.txt)
-* `filter.txt` was taken from [this list](https://github.com/dwyl/english-words/blob/22d7c41119076750a96fca2acd664ed994cc0a75/words_alpha.txt)
-* `complexity_model` was trained on data from the Cambridge Exams readability dataset, found [here](https://ilexir.co.uk/datasets/index.html)
-
-# Acknowledgements
-This paper reports on research supported by Cambridge University Press & Assessment. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the [University of Cambridge Research Computing Service](www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and [DiRAC funding from the Science and Technology Facilities Council](www.dirac.ac.uk).

ControllableComplexityChatbot-huggingface-compatible/app.py

@@ -1,96 +0,0 @@
-import gradio as gr
-import os
-import json
-from parlai.core.opt import Opt
-from parlai.zoo.blender.blender_3B import download
-from parlai.core.agents import Agent
-from parlai.core.params import ParlaiParser
-from parlai.core.worlds import DialogPartnerWorld
-from controllable_blender import ControllableBlender
-from huggingface_hub import snapshot_download
-
-snapshot_download(repo_id="shivansarora/ControllableBlender", local_dir="ParlAI/data/models/blender/blender_3B")
-
-# Load options
-agent_opt = json.load(open("blender_3B.opt", 'r'))
-download(agent_opt["datapath"])
-conversation_state = {"world": None, "human_agent": None}
-
-class GradioHumanAgent(Agent):
-    def __init__(self, opt):
-        super().__init__(opt)
-        self.msg = None
-
-    def observe(self, msg):
-        return msg
-
-    def act(self):
-        return {"text": self.msg, "episode_done": False}
-
-
-def init_world(cefr, inference_type):
-    opt = agent_opt.copy()
-    opt["rerank_cefr"] = cefr
-    opt["inference"] = inference_type
-
-    # Settings for rerank methods (not used if "inference" == "vocab")
-    opt["rerank_tokenizer"] = "distilroberta-base"        # Tokenizer from Huggingface Transformers. Must be compatible with "rerank_model"
-    opt["rerank_model"] = "complexity_model"              # Model fine-tuned on complexity data
-    opt["rerank_model_device"] = "cuda"                   # Device for complexity model
-    opt["penalty_stddev"] = 2                             # Controls how harshly sub-tokens are penalised (lower = harsher). Use -1 to remove penalties
-    opt["filter_path"] = "data/filter.txt"                # Path to list of English words to ensure OOV words are not generated. Capitalised words are ignored. Use empty string to remove filter
-
-    # Settings for vocab methods (not used if "inference" == "rerank")
-    opt["wordlist_path"] = "data/sample_wordlist.txt"     # Path to list of vocab the chatbot is restricted to    
-   
-    # Same top-k sampling configs for all settings described in the paper
-    opt["beam_size"] = 20
-    opt["topk"] = 40
-
-    human_agent = GradioHumanAgent(opt)
-    model_agent = ControllableBlender(opt)
-    world = DialogPartnerWorld(opt, [human_agent, model_agent])
-
-    return human_agent, world
-
-def chat(user_input, cefr, inference_type, history):
-    if conversation_state["world"] is None:
-
-        human_agent, world = init_world(cefr, inference_type)
-        conversation_state["world"] = world
-        conversation_state["human_agent"] = human_agent
-
-    conversation_state["human_agent"].msg = user_input
-
-    conversation_state["world"].parley()
-
-    bot_reply = conversation_state["world"].acts[1].get("text", "")
-    history.append([user_input, bot_reply.strip()])
-    return history, history
-
-def reset_chat():
-    conversation_state["world"] = None
-    conversation_state["human_agent"] = None
-    return []
-
-with gr.Blocks() as demo:
-    cefr = gr.Dropdown(["A1", "A2", "B1", "B2", "C1", "C2"], label="CEFR", value="B2")
-    inference_type = gr.Dropdown(["rerank", "vocab"], label="Inference", value="rerank")
-    user_input = gr.Textbox(label="your message")
-    chatbot = gr.Chatbot(label="Controllable Complexity Chatbot")
-    send_btn = gr.Button("Send")
-
-    state = gr.State([])
-
-    def user_chat(message, cefr_level, infer_type, history):
-        # call your chat function here
-        new_history, _ = chat(message, cefr_level, infer_type, history)
-        return new_history, new_history
-
-    send_btn.click(
-        fn=user_chat,
-        inputs=[user_input, cefr, inference_type, state],
-        outputs=[chatbot, state]
-    )
-
-demo.launch(share=True)

ControllableComplexityChatbot-huggingface-compatible/blender_3B.opt

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-{
-  "init_opt": null,
-  "show_advanced_args": false,
-  "task": "interactive",
-  "datatype": "test",
-  "image_mode": "raw",
-  "numthreads": 1,
-  "hide_labels": false,
-  "batchsize": 128,
-  "dynamic_batching": null,
-  "datapath": "ParlAI/data",
-  "model": "transformer/generator",
-  "model_file": "ParlAI/data/models/blender/blender_3B/model",
-  "init_model": "/checkpoint/parlai/zoo/meena/20200319_meenav0data_tall_2.7B_adamoptimizer/20200319_13.3ppl_200kupdates/model",
-  "dict_class": "parlai.core.dict:DictionaryAgent",
-  "evaltask": null,
-  "eval_batchsize": null,
-  "display_examples": false,
-  "num_epochs": -1,
-  "max_train_time": 27647.999999999996,
-  "validation_every_n_secs": -1,
-  "save_every_n_secs": -1,
-  "save_after_valid": true,
-  "validation_every_n_epochs": 0.25,
-  "validation_max_exs": -1,
-  "short_final_eval": false,
-  "validation_patience": 10,
-  "validation_metric": "ppl",
-  "validation_metric_mode": "min",
-  "validation_cutoff": 1.0,
-  "validation_share_agent": false,
-  "metrics": "default",
-  "aggregate_micro": false,
-  "tensorboard_log": false,
-  "dict_maxexs": -1,
-  "dict_include_valid": false,
-  "dict_include_test": false,
-  "log_every_n_secs": 10.0,
-  "image_size": 256,
-  "image_cropsize": 224,
-  "label_type": "response",
-  "include_knowledge": true,
-  "include_checked_sentence": true,
-  "include_knowledge_separator": false,
-  "num_topics": 5,
-  "train_experiencer_only": false,
-  "remove_political_convos": false,
-  "embedding_size": 2560,
-  "n_layers": 2,
-  "ffn_size": 10240,
-  "dropout": 0.1,
-  "attention_dropout": 0.0,
-  "relu_dropout": 0.0,
-  "n_heads": 32,
-  "learn_positional_embeddings": false,
-  "embeddings_scale": true,
-  "n_positions": 128,
-  "n_segments": 0,
-  "variant": "prelayernorm",
-  "activation": "gelu",
-  "output_scaling": 1.0,
-  "share_word_embeddings": true,
-  "n_encoder_layers": 2,
-  "n_decoder_layers": 24,
-  "model_parallel": true,
-  "beam_size": 20,
-  "beam_min_length": 20,
-  "beam_context_block_ngram": 3,
-  "beam_block_ngram": 3,
-  "beam_length_penalty": 0.65,
-  "skip_generation": false,
-  "inference": "topk",
-  "topk": 40,
-  "topp": 0.9,
-  "beam_delay": 30,
-  "temperature": 1.0,
-  "compute_tokenized_bleu": false,
-  "embedding_type": "random",
-  "embedding_projection": "random",
-  "fp16": true,
-  "fp16_impl": "mem_efficient",
-  "force_fp16_tokens": true,
-  "optimizer": "mem_eff_adam",
-  "learningrate": 7e-06,
-  "gradient_clip": 0.1,
-  "adam_eps": 1e-08,
-  "adafactor_eps": [
-    1e-30,
-    0.001
-  ],
-  "momentum": 0,
-  "nesterov": true,
-  "nus": [
-    0.7
-  ],
-  "betas": [
-    0.9,
-    0.999
-  ],
-  "weight_decay": null,
-  "rank_candidates": false,
-  "truncate": 128,
-  "text_truncate": 128,
-  "label_truncate": 128,
-  "history_size": -1,
-  "person_tokens": false,
-  "split_lines": false,
-  "use_reply": "label",
-  "add_p1_after_newln": false,
-  "delimiter": "  ",
-  "history_add_global_end_token": "end",
-  "gpu": -1,
-  "no_cuda": false,
-  "dict_file": "ParlAI/data/models/blender/blender_3B/model.dict",
-  "dict_initpath": null,
-  "dict_language": "english",
-  "dict_max_ngram_size": -1,
-  "dict_minfreq": 0,
-  "dict_maxtokens": -1,
-  "dict_nulltoken": "__null__",
-  "dict_starttoken": "__start__",
-  "dict_endtoken": "__end__",
-  "dict_unktoken": "__unk__",
-  "dict_tokenizer": "bytelevelbpe",
-  "dict_lower": false,
-  "bpe_debug": false,
-  "dict_textfields": "text,labels",
-  "bpe_vocab": "ParlAI/data/models/blender/blender_3B/model.dict-vocab.json",
-  "bpe_merge": "ParlAI/data/models/blender/blender_3B/model.dict-merges.txt",
-  "bpe_add_prefix_space": true,
-  "lr_scheduler": "reduceonplateau",
-  "lr_scheduler_patience": 3,
-  "lr_scheduler_decay": 0.5,
-  "max_lr_steps": -1,
-  "invsqrt_lr_decay_gamma": -1,
-  "warmup_updates": 100,
-  "warmup_rate": 0.0001,
-  "update_freq": 2,
-  "parlai_home": "ParlAI/",
-  "starttime": "Mar31_06-04",
-  "beam_block_full_context": false,
-  "batchindex": 127,
-  "dict_loaded": true
-}

ControllableComplexityChatbot-huggingface-compatible/complexity_model/config.json

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-{
-  "_name_or_path": "distilroberta-base",
-  "architectures": [
-    "RobertaForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "eos_token_id": 2,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "LABEL_0"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "LABEL_0": 0
-  },
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 514,
-  "model_type": "roberta",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 6,
-  "pad_token_id": 1,
-  "position_embedding_type": "absolute",
-  "transformers_version": "4.5.1",
-  "type_vocab_size": 1,
-  "use_cache": true,
-  "vocab_size": 50265
-}

ControllableComplexityChatbot-huggingface-compatible/complexity_model/pytorch_model.bin

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-version https://git-lfs.github.com/spec/v1
-oid sha256:4463a45e88dedd693c26e9a16830c6bc62fcf60717c8c38646ca324ddc12c0a7
-size 134

ControllableComplexityChatbot-huggingface-compatible/complexity_model/training_args.bin

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-version https://git-lfs.github.com/spec/v1
-oid sha256:d1ec2aaba4756da99f79eab56d1d947d588b52e4c7565d971476688fc9a0df83
-size 2287

ControllableComplexityChatbot-huggingface-compatible/controllable_blender/__init__.py

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-from .controllable_blender import ControllableBlender

ControllableComplexityChatbot-huggingface-compatible/controllable_blender/controllable_blender.py

@@ -1,107 +0,0 @@
-from parlai.core.opt import Opt
-from parlai.utils.typing import TShared
-from parlai.agents.transformer.transformer import TransformerGeneratorAgent
-
-from .generation_methods import VocabTopKSampling, RerankedTopKSampling
-from .generation_utils import Wordlist, Reranker, load_wordlist, cefr_to_int
-
-class ControllableBlender(TransformerGeneratorAgent):
-    def __init__(self, opt: Opt, shared: TShared = None):
-        super().__init__(opt, shared)
-
-        if opt.get("inference", None) == "vocab":
-            wordlist_path = opt.get("wordlist_path", None)
-            assert wordlist_path, "Please provide path to vocab list, in order to use inference method 'vocab'"
-
-            allowed_words = load_wordlist(wordlist_path)
-            self.wordlist = Wordlist(allowed_words, self.dict)
-
-        elif opt.get("inference", None) == "rerank":
-            cefr = opt.get("rerank_cefr", None)
-            assert cefr, "Please provide CEFR level, in order to use inference method 'rerank'"
-
-            rerank_tokenizer = opt.get("rerank_tokenizer", None)
-            rerank_model = opt.get("rerank_model", None)
-            assert rerank_model, "Please provide path to directory containing model weights, in order to use inference method 'rerank'"
-
-            device = opt.get("complexity_model_device", None)
-            penalty_stddev = opt.get("penalty_stddev", None)
-            text_truncate = opt.get("text_truncate", None)
-
-            word_filter = None
-            filter_path = opt.get("filter_path", "")
-            if filter_path:
-                word_filter = load_wordlist(filter_path)
-
-            exempt_tokens = [self.dict.tok2ind.get(self.dict.null_token),
-                             self.dict.tok2ind.get(self.dict.start_token),
-                             self.dict.tok2ind.get(self.dict.end_token),
-                             self.dict.tok2ind.get(self.dict.unk_token)]
-
-            if penalty_stddev < 0:
-                exempt_tokens = "all"
-
-            self.reranker = Reranker(cefr=cefr_to_int(cefr),
-                                     model=rerank_model,
-                                     tokenizer=rerank_tokenizer,
-                                     device=device,
-                                     text_truncate=text_truncate,
-                                     exempt_tokens=exempt_tokens,
-                                     penalty_stddev=penalty_stddev,
-                                     vocab_size=len(self.dict),
-                                     word_filter=word_filter)
-
-        else:
-            raise ValueError(f"Inference method {opt.get('inference', None)} does not exist. "
-                             f"Please use 'vocab' or 'rerank'.")
-
-
-    def _treesearch_factory(self, device, verbose=False):
-        method = self.opt.get('inference', 'greedy')
-        beam_size = self.opt.get('beam_size', 1)
-        if method == 'vocab':
-            return VocabTopKSampling(
-                k=self.opt.get('topk', 40),
-                wordlist=self.wordlist,
-                beam_size=beam_size,
-                min_length=self.beam_min_length,
-                block_ngram=self.beam_block_ngram,
-                context_block_ngram=self.beam_context_block_ngram,
-                length_penalty=self.opt.get('beam_length_penalty', 0.65),
-                padding_token=self.NULL_IDX,
-                bos_token=self.START_IDX,
-                eos_token=self.END_IDX,
-                device=device,
-                verbose=verbose,
-            )
-        elif method == "rerank":
-            return RerankedTopKSampling(
-                k=self.opt.get('topk', 40),
-                reranker=self.reranker,
-                tokenids_to_text=self._v2t,
-                beam_size=beam_size,
-                min_length=self.beam_min_length,
-                block_ngram=self.beam_block_ngram,
-                context_block_ngram=self.beam_context_block_ngram,
-                length_penalty=self.opt.get('beam_length_penalty', 0.65),
-                padding_token=self.NULL_IDX,
-                bos_token=self.START_IDX,
-                eos_token=self.END_IDX,
-                device=device,
-                verbose=verbose,
-            )
-        else:
-            return super()._treesearch_factory(device, verbose=verbose)
-
-    def share(self):
-        """
-        Share internal states between parent and child instances.
-        """
-        shared = super().share()
-        if hasattr(self, 'wordlist'):
-            shared['wordlist'] = self.wordlist
-        if hasattr(self, 'reranker'):
-            shared['reranker'] = self.reranker
-        return shared
-
-

ControllableComplexityChatbot-huggingface-compatible/controllable_blender/generation_methods.py

@@ -1,200 +0,0 @@
-import math
-from operator import attrgetter
-from typing import Callable
-
-import numpy as np
-import regex
-from scipy.stats import rankdata
-
-import torch
-from parlai.core.torch_generator_agent import TopKSampling, TreeSearch, _HypothesisTail, _PathSelection
-from parlai.utils.torch import neginf
-
-from .generation_utils import Reranker, Wordlist
-
-class VocabTopKSampling(TopKSampling):
-
-    def __init__(self,
-                 k: int,
-                 wordlist: Wordlist,
-                 *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.k = k
-        self.wordlist = wordlist
-
-    def select_paths(self, logprobs, prior_scores, current_length) -> _PathSelection:
-        """
-        Select the next vocabulary item in these beams.
-        """
-        if len(self.all_scores) > 1:
-            for hypid in range(self.beam_size):
-                allowed_ids = self.wordlist.get_allowed_ids(self.partial_hyps[hypid])
-
-                neginf_assign = torch.ones(logprobs.shape[1], dtype=bool)
-                neginf_assign[allowed_ids] = False
-
-                logprobs[hypid, neginf_assign] = neginf(logprobs.dtype)
-
-        return super().select_paths(logprobs, prior_scores, current_length)
-
-
-class RerankedTopKSampling(TreeSearch):
-    def __init__(self,
-                 k: int,
-                 reranker: Reranker,
-                 tokenids_to_text: Callable,
-                 *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.k = k
-        self.reranker = reranker
-        self.tokenids_to_text = tokenids_to_text
-
-    def select_paths(self, logprobs, prior_scores, current_length) -> _PathSelection:
-        """
-        Select the next vocabulary item in these beams.
-        Adapted from top-k sampling https://github.com/facebookresearch/ParlAI/blob/054a0fff8183e357727dc7a91682496734badb7f/parlai/core/torch_generator_agent.py
-        """
-        values, indices = logprobs.topk(self.k, dim=-1)
-        probs = torch.softmax(values, dim=-1)
-
-        all_penalties = self.reranker.token_penalties.repeat(self.beam_size, 1).to(probs.device)
-        penalties = torch.gather(all_penalties, -1, indices)
-        penalised_probs = torch.mul(probs, penalties)
-
-        choices = torch.multinomial(penalised_probs, 1)[:, 0]
-        hyp_ids = torch.arange(logprobs.size(0)).to(logprobs.device)
-        tok_ids = indices[hyp_ids, choices]
-        scores = values[hyp_ids, choices]
-        best_scores = prior_scores.expand_as(scores) + scores
-
-        token_details: Optional[List[_PathSelectionTokenDetails]] = None
-        if self.verbose:
-            tok_logprobs = probs[hyp_ids, choices].log().view(-1).cpu().numpy()
-            tok_ranks = choices.view(-1).cpu().numpy()
-            token_details = []
-
-            for tok_logprob, tok_rank in zip(tok_logprobs, tok_ranks):
-                token_details.append(
-                    {"token_logprob": tok_logprob, "token_rank": int(tok_rank)}
-                )
-
-        return _PathSelection(
-            hypothesis_ids=hyp_ids,
-            token_ids=tok_ids,
-            scores=best_scores,
-            token_details=token_details,
-        )
-
-
-    def get_rescored_finished(self, n_best=None):
-        """
-        Adapted version of code taken from https://github.com/facebookresearch/ParlAI/blob/054a0fff8183e357727dc7a91682496734badb7f/parlai/core/torch_generator_agent.py
-        Adds complexity scoring and reranking.
-
-        Original description:
-        Return finished hypotheses according to adjusted scores.
-        Score adjustment is done according to the Google NMT paper, which
-        penalizes long utterances.
-        :param n_best:
-            number of finalized hypotheses to return
-        :return:
-            list of (tokens, score, token_metadata) 3-tuples, in sorted order, where:
-              - tokens is a tensor of token ids
-              - score is the adjusted log probability of the entire utterance
-              - token_metadata dictionary:
-                    token_logprobs -> a tensor of conditional log probabilities of tokens
-                    token_ranks -> a tensor of ranks of tokens in vocabulator, by probability, when sampled
-        """
-        # if we never actually finished, force one
-        if not self.finished:
-            self.outputs[-1][0] = self.eos
-            self.finished.append(
-                _HypothesisTail(
-                    timestep=len(self.outputs) - 1,
-                    hypid=0,
-                    score=self.all_scores[-1][0],
-                    tokenid=self.outputs[-1][0],
-                    token_details=self.token_details[0][-1]
-                    if self.token_details is not None
-                    else None,
-                )
-            )
-
-        # Calculate scores
-        hyps_str = []
-        length_penalties = []
-        for finished_item in self.finished:
-            token_ids = self._get_pretty_hypothesis(self._get_hyp_from_finished(finished_item))
-            hyps_str.append(self.tokenids_to_text(token_ids))
-            current_length = finished_item.timestep + 1
-            # these weights are from Google NMT paper
-            length_penalty = math.pow((1 + current_length) / 6, self.length_penalty)
-            length_penalties.append(length_penalty)
-
-        original_scores = []
-        for i, finished_item in enumerate(self.finished):
-            current_length = finished_item.timestep + 1
-            # these weights are from Google NMT paper
-            length_penalty = math.pow((1 + current_length) / 6, self.length_penalty)
-            original_scores.append(finished_item.score.cpu() / length_penalty)
-
-        complexity_scores = self.reranker.get_complexity_scores(hyps_str)
-        complexity_ranks = rankdata(complexity_scores)
-        original_ranks = rankdata(original_scores)
-
-        combined_ranks = complexity_ranks + original_ranks
-
-
-        rescored_finished = []
-        for i, finished_item in enumerate(self.finished):
-            score = combined_ranks[i]
-            if "u/" in hyps_str[i] or "r/" in hyps_str[i]:      # Fix for Reddit language, see paper appendix
-                score = np.array(-1, dtype=combined_ranks.dtype)
-
-            if self.reranker.word_filter:
-                for word in regex.findall("(?<=[^\p{L}])\p{Ll}+", hyps_str[i]): # Find all non-capitalised words
-                    if word not in self.reranker.word_filter:
-                        score = np.array(-1, dtype=combined_ranks.dtype)
-                        break
-
-            rescored_finished.append(
-                _HypothesisTail(
-                    timestep=finished_item.timestep,
-                    hypid=finished_item.hypid,
-                    score=finished_item.score / length_penalty,
-                    tokenid=finished_item.tokenid,
-                    token_details=finished_item.token_details,
-                )
-            )
-
-        # Note: beam size is almost always pretty small, so sorting is cheap enough
-        srted = sorted(rescored_finished, key=attrgetter('score'), reverse=True)
-
-        if n_best is not None:
-            srted = srted[:n_best]
-
-        n_best_list = []
-        for hyp in srted:
-            hyp_data = self._get_hyp_from_finished(hyp)
-            token_ids = self._get_pretty_hypothesis(hyp_data)
-            token_metadata = (
-                [tok.token_details for tok in reversed(hyp_data)]
-                if self.verbose
-                else None
-            )
-            n_best_list.append((token_ids, hyp.score, token_metadata))
-
-        # check that there is at least one finished candidate
-        # and assert that each of them contains only one EOS
-        assert (
-            len(n_best_list) >= 1
-        ), f'TreeSearch returned {len(n_best_list)} candidates, must be >= 1'
-        for (pred, score, _) in n_best_list:
-            assert (pred == self.eos).sum() == 1, (
-                f'TreeSearch returned a finalized hypo with multiple end tokens '
-                f'with score {score.item():.2f}'
-            )
-
-        return n_best_list
-
-

ControllableComplexityChatbot-huggingface-compatible/controllable_blender/generation_utils.py

@@ -1,196 +0,0 @@
-import math
-import os
-import json
-from typing import List, Set, Union, Optional
-
-import numpy as np
-import torch
-from parlai.core.dict import DictionaryAgent
-from transformers import RobertaForSequenceClassification, RobertaTokenizer
-
-def cefr_to_int(cefr: str) -> int:
-    mapping = {
-        "A1": 0,
-        "A2": 1,
-        "B1": 2,
-        "B2": 3,
-        "C1": 4,
-        "C2": 5,
-    }
-    clean_cefr = cefr.upper().strip()
-    assert clean_cefr in mapping, f"CEFR must be one of {list(mapping.keys())}, not {cefr}"
-
-    return mapping[clean_cefr]
-
-
-def load_wordlist(path: str) -> List[str]:
-    """
-    Load a list of words from a text file containing one word per line
-    """
-    vocab = []
-
-    if not path:
-        return vocab
-
-    assert os.path.isfile(path)
-
-    with open(path, 'r', encoding="utf-8") as vocab_file:
-        for row in vocab_file:
-            token = row.strip()
-            vocab.append(token)
-
-    return vocab
-
-
-class Wordlist():
-    def __init__(self, allowed_words: List[str], dict_agent: DictionaryAgent):
-        self.dict_agent = dict_agent
-
-        # Identify IDs that represent a word boundary and those that don't
-        self.boundary_ids = []
-        self.non_boundary_ids = []
-
-        for idx, subtoken in dict_agent.ind2tok.items():
-            if subtoken[0] == "\u0120" or not subtoken.isalpha():
-                self.boundary_ids.append(idx)
-            else:
-                self.non_boundary_ids.append(idx)
-
-        # Identify token ID sequences that are allowed words
-        # Identify allowed continuations of sequences
-        self.allowed_sequences = []
-        self.allowed_continuations = {}
-        for word in allowed_words:
-            for word_variant in self._get_word_variants(word):
-                token_ids = dict_agent.txt2vec(word_variant)
-                self.allowed_sequences.append(repr(token_ids))
-
-                for i, idx in enumerate(token_ids[1:]):
-                    prefix = repr(token_ids[:i + 1])      # List represented as string for lookup
-                    if prefix not in self.allowed_continuations:
-                        self.allowed_continuations[prefix] = []
-                    self.allowed_continuations[prefix].append(idx)
-
-        self.allowed_sequences = set(self.allowed_sequences)
-
-
-    def get_allowed_ids(self, token_ids: List[int]) -> List[int]:
-        last_word = self._get_last_word(token_ids)
-        continuation_ids = self._get_continuation_ids(last_word)
-
-        return continuation_ids
-
-
-    def _is_word(self, token_ids: List[int]) -> bool:
-        """
-        For a given sequence of token IDs, determine whether that sequence is a complete word
-        """
-        return (token_ids == [] or repr(token_ids) in self.allowed_sequences)
-
-
-    def _get_continuation_ids(self, token_ids: List[int]) -> List[int]:
-        """
-        For a given sequence of last word token IDs, determine which token IDs the word can continue with
-        """
-        continuation_ids = []
-        if repr(token_ids) in self.allowed_continuations:
-            continuation_ids.extend(self.allowed_continuations[repr(token_ids)])
-
-        if self._is_word(token_ids) or token_ids == []:
-            continuation_ids.extend(self.boundary_ids)
-
-        return continuation_ids
-
-
-    def _get_last_word(self, token_ids: List[int]) -> List[int]:
-        """
-        Get the sequence of token IDs after the last word boundary.
-        Assumes that a word boundary is denoted by punctuation or whitespace (Ġ).
-        """
-        for i in range(-1, -len(token_ids), -1):
-            last_word = token_ids[i:]
-            check_token = self.dict_agent[last_word[0]]
-
-            if not check_token.isalpha():
-                return last_word[1:]
-
-            if check_token[0] == "Ġ":
-                return last_word
-
-        raise ValueError("Boundary token not found")
-
-
-    def _get_word_variants(self, word: str) -> Set[str]:
-        return {word, word.lower(), word.capitalize()}
-
-
-
-class Reranker():
-    def __init__(self,
-                 cefr: int,
-                 model: str,
-                 tokenizer: str = "distilroberta-base",
-                 device: Optional[str] = "cuda",
-                 text_truncate: int = 128,
-                 exempt_tokens: Union[str, List[int]] = "all",
-                 penalty_stddev: int = 2,
-                 vocab_size: int = 8008,
-                 word_filter: Optional[List[str]] = None):
-
-        self.tokenizer = RobertaTokenizer.from_pretrained(tokenizer)
-        self.model = RobertaForSequenceClassification.from_pretrained(model)
-        self.model.to(device)
-        self.device = device
-
-        self.target_cefr = cefr
-        self.text_truncate = text_truncate
-        self.word_filter = word_filter
-
-        cefr_filepath = os.path.join(os.path.dirname(__file__), 'tokens_by_cefr.json')
-        with open(cefr_filepath, 'r') as cefr_file:
-            token_cefrs = json.load(cefr_file)
-
-        if exempt_tokens == "all" or penalty_stddev < 0:      # No penalties
-            self.token_penalties = torch.tensor([[1] * vocab_size])
-        else:
-            # calculate penalties per CEFR level difference (0 = same CEFR)
-            normal_dist = torch.distributions.normal.Normal(0, penalty_stddev)
-            cefr_penalties = [math.exp(normal_dist.log_prob(torch.tensor(i))) for i in range(6)]
-
-            token_penalties = []
-            for i in range(vocab_size):
-                if i in exempt_tokens:
-                    token_penalties.append(cefr_penalties[0])
-
-                elif str(i) in token_cefrs:
-                    token_str, token_cefr = token_cefrs[str(i)]
-                    penalty = cefr_penalties[int(token_cefr - self.target_cefr)]
-
-                    if token_cefr <= self.target_cefr or not token_str.isalpha():         # ignore lower CEFR levels and punctuation/special tokens
-                        penalty = cefr_penalties[0]
-
-                    token_penalties.append(penalty)
-
-                else:       # Assume highest CEFR level if we don't have an assigned CEFR level
-                    token_penalties.append(cefr_penalties[int(5 - self.target_cefr)])
-
-            self.token_penalties = torch.tensor([token_penalties])
-
-    def get_complexity_scores(self, hyps: List[str]) -> np.ndarray:
-        model_inputs = self.tokenizer(hyps,
-                                      padding='max_length',
-                                      truncation=True,
-                                      max_length=self.text_truncate,
-                                      return_tensors='pt',
-                                      return_token_type_ids=True,
-                                      return_attention_mask=True)
-
-        model_output = self.model(input_ids=model_inputs["input_ids"].to(self.device),
-                                  attention_mask=model_inputs["attention_mask"].to(self.device),
-                                  token_type_ids=model_inputs["token_type_ids"].to(self.device))
-
-        complexity_scores = model_output.logits.cpu().numpy().flatten()
-        complexity_diffs = 5 - np.absolute(complexity_scores - self.target_cefr)      # reversed so that higher score = better
-
-        return complexity_diffs
-

ControllableComplexityChatbot-huggingface-compatible/data/sample_wordlist.txt

@@ -1,5000 +0,0 @@
-the
-of
-and
-to
-a
-in
-for
-is
-on
-that
-by
-this
-with
-i
-you
-it
-not
-or
-be
-are
-from
-at
-as
-your
-all
-have
-new
-more
-an
-was
-we
-will
-home
-can
-us
-about
-if
-page
-my
-has
-search
-free
-but
-our
-one
-other
-do
-no
-information
-time
-they
-site
-he
-up
-may
-what
-which
-their
-news
-out
-use
-any
-there
-see
-only
-so
-his
-when
-contact
-here
-business
-who
-web
-also
-now
-help
-get
-pm
-view
-online
-c
-e
-first
-am
-been
-would
-how
-were
-me
-s
-services
-some
-these
-click
-its
-like
-service
-x
-than
-find
-price
-date
-back
-top
-people
-had
-list
-name
-just
-over
-state
-year
-day
-into
-email
-two
-health
-n
-world
-re
-next
-used
-go
-b
-work
-last
-most
-products
-music
-buy
-data
-make
-them
-should
-product
-system
-post
-her
-city
-t
-add
-policy
-number
-such
-please
-available
-copyright
-support
-message
-after
-best
-software
-then
-jan
-good
-video
-well
-d
-where
-info
-rights
-public
-books
-high
-school
-through
-m
-each
-links
-she
-review
-years
-order
-very
-privacy
-book
-items
-company
-r
-read
-group
-need
-many
-user
-said
-de
-does
-set
-under
-general
-research
-university
-january
-mail
-full
-map
-reviews
-program
-life
-know
-games
-way
-days
-management
-p
-part
-could
-great
-united
-hotel
-real
-f
-item
-international
-center
-ebay
-must
-store
-travel
-comments
-made
-development
-report
-off
-member
-details
-line
-terms
-before
-hotels
-did
-send
-right
-type
-because
-local
-those
-using
-results
-office
-education
-national
-car
-design
-take
-posted
-internet
-address
-community
-within
-states
-area
-want
-phone
-dvd
-shipping
-reserved
-subject
-between
-forum
-family
-l
-long
-based
-w
-code
-show
-o
-even
-black
-check
-special
-prices
-website
-index
-being
-women
-much
-sign
-file
-link
-open
-today
-technology
-south
-case
-project
-same
-pages
-uk
-version
-section
-own
-found
-sports
-house
-related
-security
-both
-g
-county
-american
-photo
-game
-members
-power
-while
-care
-network
-down
-computer
-systems
-three
-total
-place
-end
-following
-download
-h
-him
-without
-per
-access
-think
-north
-resources
-current
-posts
-big
-media
-law
-control
-water
-history
-pictures
-size
-art
-personal
-since
-including
-guide
-shop
-directory
-board
-location
-change
-white
-text
-small
-rating
-rate
-government
-children
-during
-usa
-return
-students
-v
-shopping
-account
-times
-sites
-level
-digital
-profile
-previous
-form
-events
-love
-old
-john
-main
-call
-hours
-image
-department
-title
-description
-non
-k
-y
-insurance
-another
-why
-shall
-property
-class
-cd
-still
-money
-quality
-every
-listing
-content
-country
-private
-little
-visit
-save
-tools
-low
-reply
-customer
-december
-compare
-movies
-include
-college
-value
-article
-york
-man
-card
-jobs
-provide
-j
-food
-source
-author
-different
-press
-u
-learn
-sale
-around
-print
-course
-job
-canada
-process
-teen
-room
-stock
-training
-too
-credit
-point
-join
-science
-men
-categories
-advanced
-west
-sales
-look
-english
-left
-team
-estate
-box
-conditions
-select
-windows
-photos
-gay
-thread
-week
-category
-note
-live
-large
-gallery
-table
-register
-however
-june
-october
-november
-market
-library
-really
-action
-start
-series
-model
-features
-air
-industry
-plan
-human
-provided
-tv
-yes
-required
-second
-hot
-accessories
-cost
-movie
-forums
-march
-la
-september
-better
-say
-questions
-july
-yahoo
-going
-medical
-test
-friend
-come
-dec
-server
-pc
-study
-application
-cart
-staff
-articles
-san
-feedback
-again
-play
-looking
-issues
-april
-never
-users
-complete
-street
-topic
-comment
-financial
-things
-working
-against
-standard
-tax
-person
-below
-mobile
-less
-got
-blog
-party
-payment
-equipment
-login
-student
-let
-programs
-offers
-legal
-above
-recent
-park
-stores
-side
-act
-problem
-red
-give
-memory
-performance
-social
-q
-august
-quote
-language
-story
-sell
-options
-experience
-rates
-create
-key
-body
-young
-america
-important
-field
-few
-east
-paper
-single
-ii
-age
-activities
-club
-example
-girls
-additional
-password
-z
-latest
-something
-road
-gift
-question
-changes
-night
-ca
-hard
-texas
-oct
-pay
-four
-poker
-status
-browse
-issue
-range
-building
-seller
-court
-february
-always
-result
-audio
-light
-write
-war
-nov
-offer
-blue
-groups
-al
-easy
-given
-files
-event
-release
-analysis
-request
-fax
-china
-making
-picture
-needs
-possible
-might
-professional
-yet
-month
-major
-star
-areas
-future
-space
-committee
-hand
-sun
-cards
-problems
-london
-washington
-meeting
-rss
-become
-interest
-id
-child
-keep
-enter
-california
-share
-similar
-garden
-schools
-million
-added
-reference
-companies
-listed
-baby
-learning
-energy
-run
-delivery
-net
-popular
-term
-film
-stories
-put
-computers
-journal
-reports
-co
-try
-welcome
-central
-images
-president
-notice
-original
-head
-radio
-until
-cell
-color
-self
-council
-away
-includes
-track
-australia
-discussion
-archive
-once
-others
-entertainment
-agreement
-format
-least
-society
-months
-log
-safety
-friends
-sure
-faq
-trade
-edition
-cars
-messages
-marketing
-tell
-further
-updated
-association
-able
-having
-provides
-david
-fun
-already
-green
-studies
-close
-common
-drive
-specific
-several
-gold
-feb
-living
-sep
-collection
-called
-short
-arts
-lot
-ask
-display
-limited
-powered
-solutions
-means
-director
-daily
-beach
-past
-natural
-whether
-due
-et
-electronics
-five
-upon
-period
-planning
-database
-says
-official
-weather
-mar
-land
-average
-done
-technical
-window
-france
-pro
-region
-island
-record
-direct
-microsoft
-conference
-environment
-records
-st
-district
-calendar
-costs
-style
-url
-front
-statement
-update
-parts
-aug
-ever
-downloads
-early
-miles
-sound
-resource
-present
-applications
-either
-ago
-document
-word
-works
-material
-bill
-apr
-written
-talk
-federal
-hosting
-rules
-final
-adult
-tickets
-thing
-centre
-requirements
-via
-cheap
-kids
-finance
-true
-minutes
-else
-mark
-third
-rock
-gifts
-europe
-reading
-topics
-bad
-individual
-tips
-plus
-auto
-cover
-usually
-edit
-together
-videos
-percent
-fast
-function
-fact
-unit
-getting
-global
-tech
-meet
-far
-economic
-en
-player
-projects
-lyrics
-often
-subscribe
-submit
-germany
-amount
-watch
-included
-feel
-though
-bank
-risk
-thanks
-everything
-deals
-various
-words
-linux
-jul
-production
-commercial
-james
-weight
-town
-heart
-advertising
-received
-choose
-treatment
-newsletter
-archives
-points
-knowledge
-magazine
-error
-camera
-jun
-girl
-currently
-construction
-toys
-registered
-clear
-golf
-receive
-domain
-methods
-chapter
-makes
-protection
-policies
-loan
-wide
-beauty
-manager
-india
-position
-taken
-sort
-listings
-models
-michael
-known
-half
-cases
-step
-engineering
-florida
-simple
-quick
-none
-wireless
-license
-paul
-friday
-lake
-whole
-annual
-published
-later
-basic
-sony
-shows
-corporate
-google
-church
-method
-purchase
-customers
-active
-response
-practice
-hardware
-figure
-materials
-fire
-holiday
-chat
-enough
-designed
-along
-among
-death
-writing
-speed
-html
-countries
-loss
-face
-brand
-discount
-higher
-effects
-created
-remember
-standards
-oil
-bit
-yellow
-political
-increase
-advertise
-kingdom
-base
-near
-environmental
-thought
-stuff
-french
-storage
-oh
-japan
-doing
-loans
-shoes
-entry
-stay
-nature
-orders
-availability
-africa
-summary
-turn
-mean
-growth
-notes
-agency
-king
-monday
-european
-activity
-copy
-although
-drug
-pics
-western
-income
-force
-cash
-employment
-overall
-bay
-river
-commission
-ad
-package
-contents
-seen
-players
-engine
-port
-album
-regional
-stop
-supplies
-started
-administration
-bar
-institute
-views
-plans
-double
-dog
-build
-screen
-exchange
-types
-soon
-sponsored
-lines
-electronic
-continue
-across
-benefits
-needed
-season
-apply
-someone
-held
-ny
-anything
-printer
-condition
-effective
-believe
-organization
-effect
-asked
-eur
-mind
-sunday
-selection
-casino
-pdf
-lost
-tour
-menu
-volume
-cross
-anyone
-mortgage
-hope
-silver
-corporation
-wish
-inside
-solution
-mature
-role
-rather
-weeks
-addition
-came
-supply
-nothing
-certain
-usr
-executive
-running
-lower
-necessary
-union
-jewelry
-according
-dc
-clothing
-mon
-com
-particular
-fine
-names
-robert
-homepage
-hour
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-metabolism
-keith
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-bigger
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-exceed
-bs
-rod
-saudi
-fault
-cuba
-hrs
-preliminary
-districts
-introduce
-silk
-promotional

ControllableComplexityChatbot-huggingface-compatible/requirements.txt

@@ -1,9 +0,0 @@
-torch
-transformers
-scipy
-regex
-gradio
-numpy
-setuptools
-iopath
-git+https://github.com/facebookresearch/ParlAI.git@1.6.0