updating for c2
This commit is contained in:
Matthew Gaughan
parent
95fd1f1e5d
commit
3ac2d43a28
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library(dplyr)
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library(ggplot2)
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phab_data_path <- "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/0217_ve_phab_comments.csv"
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phab_data <- read.csv(phab_data_path, header=TRUE)
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phab_data <- phab_data |>
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mutate(has_ref = grepl("visualeditor|VE|ve|VisualEditor", comment_text)) |>
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mutate(has_bot_ref = grepl("bots|scripts|gadgets", comment_text)) |>
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mutate(timestamp = as.POSIXct(date_created, origin = "1970-01-01", tz = "UTC")) |>
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mutate(comment_id = row_number())|>
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filter(date_created < 1383264000 & date_created > 1351728000)
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#looking at all data between 11-1-2012 and 11-1-2013
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length(unique(phab_data$date_created))
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#g <- ggplot(phab_data, aes(x=timestamp, y=has_bot_ref)) +
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# geom_point(alpha = 0.5) +
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# theme_minimal()
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#g
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library(udpipe)
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#library(rsyntax) https://github.com/vanatteveldt/rsyntax?tab=readme-ov-file
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library(tidytext)
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library(dplyr)
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library(stringr)
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# we first need to transform our comment level of analysis into sentences
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sentence_level_data <- phab_data |>
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unnest_tokens(sentence, comment_text, token = "sentences") |>
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group_by(comment_id) |>
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mutate(sentence_id = row_number())|>
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dplyr::select(-has_bot_ref, -has_ref)|>
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mutate(has_ref = grepl("visualeditor|VE|ve|VisualEditor", sentence)) |>
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mutate(has_bot_ref = grepl("bots|scripts|gadgets", sentence)) |>
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ungroup()
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library(udpipe)
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library(rsyntax)
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# Load necessary libraries
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library(spacyr)
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spacy_install()
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#we only care about stuff that mentions VE rn, then tokenize
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sentence_level_data <- sentence_level_data |>
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filter(has_ref == TRUE) |>
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mutate(sentence_tokens = udpipe(sentence, "english"))
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b270bd36-529e-4595-a780-ef6c8151c31f",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/torch/cuda/__init__.py:734: UserWarning: Can't initialize NVML\n",
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" warnings.warn(\"Can't initialize NVML\")\n"
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]
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}
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],
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"source": [
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"import pandas as pd \n",
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"import spacy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f6448c6f-2b5d-45f5-a32e-b3b47c16ef85",
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"metadata": {},
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"outputs": [],
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"source": [
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"phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/0228_ve_phab_comments.csv\"\n",
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"phab_df = pd.read_csv(phab_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f32f6eed-3aeb-4b05-8d40-7ed85e7235c5",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/spacy/util.py:910: UserWarning: [W095] Model 'en_coreference_web_trf' (3.4.0a2) was trained with spaCy v3.3.0 and may not be 100% compatible with the current version (3.7.5). If you see errors or degraded performance, download a newer compatible model or retrain your custom model with the current spaCy version. For more details and available updates, run: python -m spacy validate\n",
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" warnings.warn(warn_msg)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<spacy_experimental.coref.span_resolver_component.SpanResolver at 0x1495edce13c0>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nlp = spacy.load(\"en_core_web_trf\")\n",
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"nlp_coref = spacy.load(\"en_coreference_web_trf\")\n",
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"\n",
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"# use replace_listeners for the coref components\n",
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"nlp_coref.replace_listeners(\"transformer\", \"coref\", [\"model.tok2vec\"])\n",
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"nlp_coref.replace_listeners(\"transformer\", \"span_resolver\", [\"model.tok2vec\"])\n",
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"\n",
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"# we won't copy over the span cleaner - this keeps the head cluster information, which we want\n",
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"nlp.add_pipe(\"merge_entities\")\n",
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"nlp.add_pipe(\"coref\", source=nlp_coref)\n",
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"nlp.add_pipe(\"span_resolver\", source=nlp_coref)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"id": "a5b062d8-2d26-4a3e-a84c-ba0eaf6eb436",
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://github.com/explosion/spaCy/discussions/13572\n",
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"# https://github.com/explosion/spaCy/issues/13111 \n",
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"# https://explosion.ai/blog/coref\n",
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"# https://gist.github.com/thomashacker/b5dd6042c092e0a22c2b9243a64a2466\n",
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"doc = nlp(\"John is frustrated with the VisualEditor project, he thinks it doesn't work.\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"id": "999e1656-0036-4ba2-bedf-f54493f67790",
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://gist.github.com/thomashacker/b5dd6042c092e0a22c2b9243a64a2466\n",
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"from spacy.tokens import Doc\n",
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"# Define lightweight function for resolving references in text\n",
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"def resolve_references(doc: Doc) -> str:\n",
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" \"\"\"Function for resolving references with the coref ouput\n",
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" doc (Doc): The Doc object processed by the coref pipeline\n",
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" RETURNS (str): The Doc string with resolved references\n",
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" \"\"\"\n",
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" # token.idx : token.text\n",
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" token_mention_mapper = {}\n",
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" output_string = \"\"\n",
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" clusters = [\n",
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" val for key, val in doc.spans.items() if key.startswith(\"coref_cluster\")\n",
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" ]\n",
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"\n",
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" # Iterate through every found cluster\n",
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" for cluster in clusters:\n",
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" first_mention = cluster[0]\n",
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" # Iterate through every other span in the cluster\n",
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" for mention_span in list(cluster)[1:]:\n",
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" # Set first_mention as value for the first token in mention_span in the token_mention_mapper\n",
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" token_mention_mapper[mention_span[0].idx] = first_mention.text + mention_span[0].whitespace_\n",
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" \n",
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" for token in mention_span[1:]:\n",
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" # Set empty string for all the other tokens in mention_span\n",
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" token_mention_mapper[token.idx] = \"\"\n",
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"\n",
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" # Iterate through every token in the Doc\n",
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" for token in doc:\n",
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" # Check if token exists in token_mention_mapper\n",
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" if token.idx in token_mention_mapper:\n",
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" output_string += token_mention_mapper[token.idx]\n",
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" # Else add original token text\n",
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" else:\n",
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" output_string += token.text + token.whitespace_\n",
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"\n",
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" return output_string\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"id": "be476647-624b-4e95-ab62-9c6b08f85368",
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"metadata": {},
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"outputs": [],
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"source": [
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"def resolving_comment(text):\n",
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" doc = nlp(text)\n",
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" resolved_text = resolve_references(doc)\n",
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" return resolved_text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"id": "a9628b54-a1df-49cd-a365-9cba59de3421",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'i hate ve.interface, ve.interface always messes up i browser'"
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]
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},
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"resolving_comment(\"i hate ve.interface, it always messes up my browser\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "46873641-8e88-4829-9e24-4dd5e6749bd1",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/thinc/shims/pytorch.py:114: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
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" with torch.cuda.amp.autocast(self._mixed_precision):\n"
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]
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}
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],
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"source": [
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"phab_df['text'] = phab_df['comment_text'].apply(str)\n",
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"phab_df['resolved_text'] = phab_df['text'].apply(resolving_comment)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2b583feb-1c62-4c96-9ba0-2996d72e70d3",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.21"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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928
text_analysis/case2/040425_phab_comments.ipynb
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text_analysis/case2/040425_phab_comments.ipynb
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text_analysis/case2/NRC-VAD-Lexicon.txt
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text_analysis/case2/NRC-VAD-Lexicon.txt
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Load Diff
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text_analysis/case2/coref_resolution-Copy1.ipynb
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text_analysis/case2/coref_resolution-Copy1.ipynb
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@ -0,0 +1,219 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b270bd36-529e-4595-a780-ef6c8151c31f",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/torch/cuda/__init__.py:734: UserWarning: Can't initialize NVML\n",
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" warnings.warn(\"Can't initialize NVML\")\n"
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]
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}
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],
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"source": [
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"import pandas as pd \n",
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"import spacy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f6448c6f-2b5d-45f5-a32e-b3b47c16ef85",
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"metadata": {},
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"outputs": [],
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"source": [
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"phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/0228_ve_phab_comments.csv\"\n",
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"phab_df = pd.read_csv(phab_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f32f6eed-3aeb-4b05-8d40-7ed85e7235c5",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/spacy/util.py:910: UserWarning: [W095] Model 'en_coreference_web_trf' (3.4.0a2) was trained with spaCy v3.3.0 and may not be 100% compatible with the current version (3.7.5). If you see errors or degraded performance, download a newer compatible model or retrain your custom model with the current spaCy version. For more details and available updates, run: python -m spacy validate\n",
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" warnings.warn(warn_msg)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<spacy_experimental.coref.span_resolver_component.SpanResolver at 0x1495edce13c0>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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|
"source": [
|
||||||
|
"nlp = spacy.load(\"en_core_web_trf\")\n",
|
||||||
|
"nlp_coref = spacy.load(\"en_coreference_web_trf\")\n",
|
||||||
|
"\n",
|
||||||
|
"# use replace_listeners for the coref components\n",
|
||||||
|
"nlp_coref.replace_listeners(\"transformer\", \"coref\", [\"model.tok2vec\"])\n",
|
||||||
|
"nlp_coref.replace_listeners(\"transformer\", \"span_resolver\", [\"model.tok2vec\"])\n",
|
||||||
|
"\n",
|
||||||
|
"# we won't copy over the span cleaner - this keeps the head cluster information, which we want\n",
|
||||||
|
"nlp.add_pipe(\"merge_entities\")\n",
|
||||||
|
"nlp.add_pipe(\"coref\", source=nlp_coref)\n",
|
||||||
|
"nlp.add_pipe(\"span_resolver\", source=nlp_coref)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 57,
|
||||||
|
"id": "a5b062d8-2d26-4a3e-a84c-ba0eaf6eb436",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# https://github.com/explosion/spaCy/discussions/13572\n",
|
||||||
|
"# https://github.com/explosion/spaCy/issues/13111 \n",
|
||||||
|
"# https://explosion.ai/blog/coref\n",
|
||||||
|
"# https://gist.github.com/thomashacker/b5dd6042c092e0a22c2b9243a64a2466\n",
|
||||||
|
"doc = nlp(\"John is frustrated with the VisualEditor project, he thinks it doesn't work.\")\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 71,
|
||||||
|
"id": "999e1656-0036-4ba2-bedf-f54493f67790",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# https://gist.github.com/thomashacker/b5dd6042c092e0a22c2b9243a64a2466\n",
|
||||||
|
"from spacy.tokens import Doc\n",
|
||||||
|
"# Define lightweight function for resolving references in text\n",
|
||||||
|
"def resolve_references(doc: Doc) -> str:\n",
|
||||||
|
" \"\"\"Function for resolving references with the coref ouput\n",
|
||||||
|
" doc (Doc): The Doc object processed by the coref pipeline\n",
|
||||||
|
" RETURNS (str): The Doc string with resolved references\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" # token.idx : token.text\n",
|
||||||
|
" token_mention_mapper = {}\n",
|
||||||
|
" output_string = \"\"\n",
|
||||||
|
" clusters = [\n",
|
||||||
|
" val for key, val in doc.spans.items() if key.startswith(\"coref_cluster\")\n",
|
||||||
|
" ]\n",
|
||||||
|
"\n",
|
||||||
|
" # Iterate through every found cluster\n",
|
||||||
|
" for cluster in clusters:\n",
|
||||||
|
" first_mention = cluster[0]\n",
|
||||||
|
" # Iterate through every other span in the cluster\n",
|
||||||
|
" for mention_span in list(cluster)[1:]:\n",
|
||||||
|
" # Set first_mention as value for the first token in mention_span in the token_mention_mapper\n",
|
||||||
|
" token_mention_mapper[mention_span[0].idx] = first_mention.text + mention_span[0].whitespace_\n",
|
||||||
|
" \n",
|
||||||
|
" for token in mention_span[1:]:\n",
|
||||||
|
" # Set empty string for all the other tokens in mention_span\n",
|
||||||
|
" token_mention_mapper[token.idx] = \"\"\n",
|
||||||
|
"\n",
|
||||||
|
" # Iterate through every token in the Doc\n",
|
||||||
|
" for token in doc:\n",
|
||||||
|
" # Check if token exists in token_mention_mapper\n",
|
||||||
|
" if token.idx in token_mention_mapper:\n",
|
||||||
|
" output_string += token_mention_mapper[token.idx]\n",
|
||||||
|
" # Else add original token text\n",
|
||||||
|
" else:\n",
|
||||||
|
" output_string += token.text + token.whitespace_\n",
|
||||||
|
"\n",
|
||||||
|
" return output_string\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 72,
|
||||||
|
"id": "be476647-624b-4e95-ab62-9c6b08f85368",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def resolving_comment(text):\n",
|
||||||
|
" doc = nlp(text)\n",
|
||||||
|
" resolved_text = resolve_references(doc)\n",
|
||||||
|
" return resolved_text"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 73,
|
||||||
|
"id": "a9628b54-a1df-49cd-a365-9cba59de3421",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"'i hate ve.interface, ve.interface always messes up i browser'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 73,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"resolving_comment(\"i hate ve.interface, it always messes up my browser\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "46873641-8e88-4829-9e24-4dd5e6749bd1",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"/gscratch/scrubbed/mjilg/envs/coref-notebook/lib/python3.10/site-packages/thinc/shims/pytorch.py:114: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
|
||||||
|
" with torch.cuda.amp.autocast(self._mixed_precision):\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"phab_df['text'] = phab_df['comment_text'].apply(str)\n",
|
||||||
|
"phab_df['resolved_text'] = phab_df['text'].apply(resolving_comment)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "2b583feb-1c62-4c96-9ba0-2996d72e70d3",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"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": "3.9.21"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
1819
text_analysis/case2/ve_dependency-Copy1.ipynb
Normal file
1819
text_analysis/case2/ve_dependency-Copy1.ipynb
Normal file
File diff suppressed because one or more lines are too long
1403
text_analysis/case2/ve_phab_convos-Copy1.ipynb
Normal file
1403
text_analysis/case2/ve_phab_convos-Copy1.ipynb
Normal file
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user