{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from args import args, config\n", "from items_dataset import items_dataset\n", "from torch.utils.data import DataLoader\n", "from models import Model_Crf, Model_Softmax\n", "from transformers import AutoTokenizer\n", "from tqdm import tqdm\n", "import prediction\n", "import torch\n", "import math" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "directory = \"../model/\"\n", "model_name = \"roberta_CRF.pt\"\n", "device = torch.device('cuda', 0) if torch.cuda.is_available() else torch.device('cpu')\n", "model = Model_Crf(config).to(device)\n", "model.load_state_dict(state_dict=torch.load(directory + model_name, map_location=device))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "input_dict = [{\"span_labels\":[]}]\n", "input_dict[0][\"original_text\"] = \"\"\"貓咪犯錯後，以下5種懲罰方法很有效，飼主可以試試樂享網 2021-03-06 繼續閱讀 繼續閱讀 繼續閱讀 繼續閱讀 繼續閱讀 貓咪雖然高冷，但也是會犯錯的，那貓咪犯錯後，怎麼懲罰它才最有效呢？今天就來說一些懲罰貓咪最有效的5個方法！1、把痛感形成條件反射 這裡說的是「痛感」，而不是「暴打」。在貓咪犯錯後，寵主不需要打它，可以彈鼻頭或者是輕拍它的頭頂，給它造成痛感，這樣讓貓咪有一些畏懼心理，知道你在懲罰它。這樣時間長了，貓咪就會形成條件反射，以後就會少犯錯了。 2、大聲呵斥比起打貓，大聲呵斥貓咪會更加有效。因為貓咪對聲音很敏感，它能從主人的語氣中判斷主人的情緒，當大聲呵斥它的時候，它往往會楞一下，這時你繼續大聲呵斥它，那它就會明白你不允許它做這件事，這樣犯錯地方幾率就會減少了。 3、限制自由限制自由說白了，就是把貓咪關進籠子裡。因為貓咪都是很愛外出玩耍，當它犯錯咯，主人可以把它關進籠子裡，不搭理它，讓它自己反思。但要注意，這個方法不能經常用，而且不能把貓咪關進籠子太久。 4、利用水都知道貓咪很怕水的，所以當貓咪犯錯後，寵主也可以利用水來懲罰貓咪，這也是很效果的方法。寵主可以給貓咪臉上或是頭頂噴一些水，從而讓貓知道這個行為是錯誤的，以後就不會再犯了。 5、冷淡雖然貓咪不是很粘主人，但它還是很愛主人的，所以在貓咪犯錯後，寵主也可以採取冷淡的方法來懲罰貓。對貓咪採取不理睬、不靠近、不擁抱等策略，這樣貓咪就會知道自己錯了。當然懲罰的時間不要太長，不然貓咪就會以為你不愛它了。\"\"\"\n", "tokenizer = AutoTokenizer.from_pretrained(args.pre_model_name, add_prefix_space=True)\n", "prediction_dataset = items_dataset(tokenizer, input_dict, args.label_dict)\n", "prediction_loader = DataLoader(prediction_dataset, batch_size=args.batch_size, shuffle=True, collate_fn=prediction_dataset.collate_fn)\n", "predict_data = prediction.test_predict(prediction_loader, device, model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prediction.add_sentence_table(predict_data)\n", "print(predict_data[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def prepare_span_data(dataset):\n", " \"\"\"prepare spans labels for each sample\"\"\"\n", " for sample in dataset:\n", " spans = items_dataset.cal_agreement_span(None, agreement_table=sample[\"predict_sentence_table\"], min_agree=1, max_agree=2)\n", " sample[\"span_labels\"] = spans\n", " sample[\"original_text\"] = sample[\"text_a\"]\n", " del sample[\"text_a\"]\n", "prepare_span_data(predict_data)\n", "tokenizer = AutoTokenizer.from_pretrained(args.pre_model_name, add_prefix_space=True)\n", "prediction_dataset = items_dataset(tokenizer, predict_data, args.label_dict)\n", "prediction_loader = DataLoader(prediction_dataset, batch_size=args.batch_size, shuffle=False, collate_fn=prediction_dataset.collate_fn)\n", "\n", "index=0\n", "print(predict_data[index][\"original_text\"])\n", "print(predict_data[index][\"span_labels\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "directory = \"../model/\"\n", "model_name = \"roberta_softmax.pt\"\n", "device = torch.device('cuda', 0) if torch.cuda.is_available() else torch.device('cpu')\n", "model = Model_Softmax(config).to(device)\n", "model.load_state_dict(state_dict=torch.load(directory + model_name, map_location=device))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def rank_spans(test_loader, device, model, reverse=True):\n", " \"\"\"Calculate each span probability by e**(word average log likelihood)\"\"\"\n", " model.eval()\n", " result = []\n", " \n", " for i, test_batch in enumerate(tqdm(test_loader)):\n", " batch_text = test_batch['batch_text']\n", " input_ids = test_batch['input_ids'].to(device)\n", " token_type_ids = test_batch['token_type_ids'].to(device)\n", " attention_mask = test_batch['attention_mask'].to(device)\n", " labels = test_batch['labels']\n", " crf_mask = test_batch[\"crf_mask\"].to(device)\n", " sample_mapping = test_batch[\"overflow_to_sample_mapping\"]\n", " output = model(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=None, crf_mask=crf_mask)\n", " output = torch.nn.functional.softmax(output[0], dim=-1)\n", " \n", " #make result of every sample\n", " sample_id = 0\n", " sample_result= {\"original_text\" : test_batch['batch_text'][sample_id], \"span_ranked\" : []}\n", " for batch_id in range(len(sample_mapping)):\n", " change_sample = False\n", " \n", " #make sure status\n", " if sample_id != sample_mapping[batch_id]: change_sample = True\n", " if change_sample:\n", " sample_id = sample_mapping[batch_id]\n", " result.append(sample_result)\n", " sample_result= {\"original_text\" : test_batch['batch_text'][sample_id], \"span_ranked\" : []}\n", " \n", " encoded_spans = items_dataset.cal_agreement_span(None, agreement_table=labels[batch_id], min_agree=1, max_agree=2)\n", " #print(encoded_spans)\n", " for encoded_span in encoded_spans:\n", " #calculate span loss\n", " span_lenght = encoded_span[1]-encoded_span[0]\n", " #print(span_lenght)\n", " span_prob_table = torch.log(output[batch_id][encoded_span[0]:encoded_span[1]])\n", " if not change_sample and encoded_span[0]==0 and batch_id!=0: #span cross two tensors\n", " span_loss += span_prob_table[0][1] #Begin\n", " else:\n", " span_loss = span_prob_table[0][1] #Begin\n", " for token_id in range(1, span_prob_table.shape[0]):\n", " span_loss+=span_prob_table[token_id][2] #Inside\n", " span_loss /= span_lenght\n", " \n", " #span decode\n", " decode_start = test_batch[batch_id].token_to_chars(encoded_span[0]+1)[0]\n", " decode_end = test_batch[batch_id].token_to_chars(encoded_span[1])[0]+1\n", " #print((decode_start, decode_end))\n", " span_text = test_batch['batch_text'][sample_mapping[batch_id]][decode_start:decode_end]\n", " if not change_sample and encoded_span[0]==0 and batch_id!=0: #span cross two tensors\n", " presample = sample_result[\"span_ranked\"].pop(-1)\n", " sample_result[\"span_ranked\"].append([presample[0]+span_text, math.e**float(span_loss)])\n", " else:\n", " sample_result[\"span_ranked\"].append([span_text, math.e**float(span_loss)])\n", " result.append(sample_result)\n", " \n", " #sorted spans by probability\n", " for sample in result:\n", " sample[\"span_ranked\"] = sorted(sample[\"span_ranked\"], key=lambda x:x[1], reverse=reverse)\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "span_ranked = rank_spans(prediction_loader, device, model)\n", "for sample in span_ranked:\n", " print(sample[\"original_text\"])\n", " print(sample[\"span_ranked\"])" ] } ], "metadata": { "kernelspec": { "display_name": "for_project", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "7d6017e34087523a14d1e41a3fef2927de5697dc5dbb9b7906df99909cc5c8a1" } } }, "nbformat": 4, "nbformat_minor": 2 }