init
This commit is contained in:
3
sentiment-analysis/.gitignore
vendored
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3
sentiment-analysis/.gitignore
vendored
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@@ -0,0 +1,3 @@
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models/**
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docext/**
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results/**
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330
sentiment-analysis/hfd.sh
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330
sentiment-analysis/hfd.sh
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@@ -0,0 +1,330 @@
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#!/usr/bin/env bash
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# Color definitions
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RED='\033[0;31m'; GREEN='\033[0;32m'; YELLOW='\033[1;33m'; NC='\033[0m' # No Color
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export HF_ENDPOINT=https://hf-mirror.com
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trap 'printf "${YELLOW}\nDownload interrupted. You can resume by re-running the command.\n${NC}"; exit 1' INT
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display_help() {
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cat << EOF
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Usage:
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hfd <REPO_ID> [--include include_pattern1 include_pattern2 ...] [--exclude exclude_pattern1 exclude_pattern2 ...] [--hf_username username] [--hf_token token] [--tool aria2c|wget] [-x threads] [-j jobs] [--dataset] [--local-dir path] [--revision rev]
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Description:
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Downloads a model or dataset from Hugging Face using the provided repo ID.
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Arguments:
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REPO_ID The Hugging Face repo ID (Required)
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Format: 'org_name/repo_name' or legacy format (e.g., gpt2)
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Options:
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include/exclude_pattern The patterns to match against file path, supports wildcard characters.
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e.g., '--exclude *.safetensor *.md', '--include vae/*'.
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--include (Optional) Patterns to include files for downloading (supports multiple patterns).
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--exclude (Optional) Patterns to exclude files from downloading (supports multiple patterns).
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--hf_username (Optional) Hugging Face username for authentication (not email).
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--hf_token (Optional) Hugging Face token for authentication.
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--tool (Optional) Download tool to use: aria2c (default) or wget.
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-x (Optional) Number of download threads for aria2c (default: 4).
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-j (Optional) Number of concurrent downloads for aria2c (default: 5).
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--dataset (Optional) Flag to indicate downloading a dataset.
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--local-dir (Optional) Directory path to store the downloaded data.
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Defaults to the current directory with a subdirectory named 'repo_name'
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if REPO_ID is is composed of 'org_name/repo_name'.
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--revision (Optional) Model/Dataset revision to download (default: main).
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Example:
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hfd gpt2
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hfd bigscience/bloom-560m --exclude *.safetensors
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hfd meta-llama/Llama-2-7b --hf_username myuser --hf_token mytoken -x 4
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hfd lavita/medical-qa-shared-task-v1-toy --dataset
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hfd bartowski/Phi-3.5-mini-instruct-exl2 --revision 5_0
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EOF
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exit 1
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}
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[[ -z "$1" || "$1" =~ ^-h || "$1" =~ ^--help ]] && display_help
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REPO_ID=$1
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shift
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# Default values
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TOOL="aria2c"
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THREADS=4
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CONCURRENT=5
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HF_ENDPOINT=${HF_ENDPOINT:-"https://huggingface.co"}
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INCLUDE_PATTERNS=()
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EXCLUDE_PATTERNS=()
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REVISION="main"
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validate_number() {
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[[ "$2" =~ ^[1-9][0-9]*$ && "$2" -le "$3" ]] || { printf "${RED}[Error] $1 must be 1-$3${NC}\n"; exit 1; }
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}
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# Argument parsing
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while [[ $# -gt 0 ]]; do
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case $1 in
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--include) shift; while [[ $# -gt 0 && ! ($1 =~ ^--) && ! ($1 =~ ^-[^-]) ]]; do INCLUDE_PATTERNS+=("$1"); shift; done ;;
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--exclude) shift; while [[ $# -gt 0 && ! ($1 =~ ^--) && ! ($1 =~ ^-[^-]) ]]; do EXCLUDE_PATTERNS+=("$1"); shift; done ;;
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--hf_username) HF_USERNAME="$2"; shift 2 ;;
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--hf_token) HF_TOKEN="$2"; shift 2 ;;
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--tool)
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case $2 in
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aria2c|wget)
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TOOL="$2"
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;;
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*)
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printf "%b[Error] Invalid tool. Use 'aria2c' or 'wget'.%b\n" "$RED" "$NC"
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exit 1
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;;
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esac
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shift 2
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;;
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-x) validate_number "threads (-x)" "$2" 10; THREADS="$2"; shift 2 ;;
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-j) validate_number "concurrent downloads (-j)" "$2" 10; CONCURRENT="$2"; shift 2 ;;
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--dataset) DATASET=1; shift ;;
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--local-dir) LOCAL_DIR="$2"; shift 2 ;;
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--revision) REVISION="$2"; shift 2 ;;
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*) display_help ;;
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esac
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done
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# Generate current command string
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generate_command_string() {
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local cmd_string="REPO_ID=$REPO_ID"
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cmd_string+=" TOOL=$TOOL"
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cmd_string+=" INCLUDE_PATTERNS=${INCLUDE_PATTERNS[*]}"
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cmd_string+=" EXCLUDE_PATTERNS=${EXCLUDE_PATTERNS[*]}"
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cmd_string+=" DATASET=${DATASET:-0}"
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cmd_string+=" HF_USERNAME=${HF_USERNAME:-}"
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cmd_string+=" HF_TOKEN=${HF_TOKEN:-}"
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cmd_string+=" HF_ENDPOINT=${HF_ENDPOINT:-}"
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cmd_string+=" REVISION=$REVISION"
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echo "$cmd_string"
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}
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# Check if aria2, wget, curl are installed
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check_command() {
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if ! command -v $1 &>/dev/null; then
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printf "%b%s is not installed. Please install it first.%b\n" "$RED" "$1" "$NC"
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exit 1
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fi
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}
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check_command curl; check_command "$TOOL"
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LOCAL_DIR="${LOCAL_DIR:-${REPO_ID#*/}}"
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mkdir -p "$LOCAL_DIR/.hfd"
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if [[ "$DATASET" == 1 ]]; then
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METADATA_API_PATH="datasets/$REPO_ID"
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DOWNLOAD_API_PATH="datasets/$REPO_ID"
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CUT_DIRS=5
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else
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METADATA_API_PATH="models/$REPO_ID"
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DOWNLOAD_API_PATH="$REPO_ID"
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CUT_DIRS=4
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fi
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# Modify API URL, construct based on revision
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if [[ "$REVISION" != "main" ]]; then
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METADATA_API_PATH="$METADATA_API_PATH/revision/$REVISION"
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fi
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API_URL="$HF_ENDPOINT/api/$METADATA_API_PATH"
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METADATA_FILE="$LOCAL_DIR/.hfd/repo_metadata.json"
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# Fetch and save metadata
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fetch_and_save_metadata() {
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status_code=$(curl -L -s -w "%{http_code}" -o "$METADATA_FILE" ${HF_TOKEN:+-H "Authorization: Bearer $HF_TOKEN"} "$API_URL")
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RESPONSE=$(cat "$METADATA_FILE")
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if [ "$status_code" -eq 200 ]; then
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printf "%s\n" "$RESPONSE"
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else
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printf "%b[Error] Failed to fetch metadata from $API_URL. HTTP status code: $status_code.%b\n$RESPONSE\n" "${RED}" "${NC}" >&2
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rm $METADATA_FILE
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exit 1
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fi
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}
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check_authentication() {
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local response="$1"
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if command -v jq &>/dev/null; then
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local gated
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gated=$(echo "$response" | jq -r '.gated // false')
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if [[ "$gated" != "false" && ( -z "$HF_TOKEN" || -z "$HF_USERNAME" ) ]]; then
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printf "${RED}The repository requires authentication, but --hf_username and --hf_token is not passed. Please get token from https://huggingface.co/settings/tokens.\nExiting.\n${NC}"
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exit 1
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fi
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else
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if echo "$response" | grep -q '"gated":[^f]' && [[ -z "$HF_TOKEN" || -z "$HF_USERNAME" ]]; then
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printf "${RED}The repository requires authentication, but --hf_username and --hf_token is not passed. Please get token from https://huggingface.co/settings/tokens.\nExiting.\n${NC}"
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exit 1
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fi
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fi
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}
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if [[ ! -f "$METADATA_FILE" ]]; then
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printf "%bFetching repo metadata...%b\n" "$YELLOW" "$NC"
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RESPONSE=$(fetch_and_save_metadata) || exit 1
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check_authentication "$RESPONSE"
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else
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printf "%bUsing cached metadata: $METADATA_FILE%b\n" "$GREEN" "$NC"
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RESPONSE=$(cat "$METADATA_FILE")
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check_authentication "$RESPONSE"
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fi
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should_regenerate_filelist() {
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local command_file="$LOCAL_DIR/.hfd/last_download_command"
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local current_command=$(generate_command_string)
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# If file list doesn't exist, regenerate
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if [[ ! -f "$LOCAL_DIR/$fileslist_file" ]]; then
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echo "$current_command" > "$command_file"
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return 0
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fi
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# If command file doesn't exist, regenerate
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if [[ ! -f "$command_file" ]]; then
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echo "$current_command" > "$command_file"
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return 0
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fi
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# Compare current command with saved command
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local saved_command=$(cat "$command_file")
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if [[ "$current_command" != "$saved_command" ]]; then
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echo "$current_command" > "$command_file"
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return 0
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fi
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return 1
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}
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fileslist_file=".hfd/${TOOL}_urls.txt"
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if should_regenerate_filelist; then
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# Remove existing file list if it exists
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[[ -f "$LOCAL_DIR/$fileslist_file" ]] && rm "$LOCAL_DIR/$fileslist_file"
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printf "%bGenerating file list...%b\n" "$YELLOW" "$NC"
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# Convert include and exclude patterns to regex
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INCLUDE_REGEX=""
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EXCLUDE_REGEX=""
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if ((${#INCLUDE_PATTERNS[@]})); then
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INCLUDE_REGEX=$(printf '%s\n' "${INCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
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fi
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if ((${#EXCLUDE_PATTERNS[@]})); then
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EXCLUDE_REGEX=$(printf '%s\n' "${EXCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
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fi
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# Check if jq is available
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if command -v jq &>/dev/null; then
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process_with_jq() {
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if [[ "$TOOL" == "aria2c" ]]; then
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printf "%s" "$RESPONSE" | jq -r \
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--arg endpoint "$HF_ENDPOINT" \
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--arg repo_id "$DOWNLOAD_API_PATH" \
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--arg token "$HF_TOKEN" \
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--arg include_regex "$INCLUDE_REGEX" \
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--arg exclude_regex "$EXCLUDE_REGEX" \
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--arg revision "$REVISION" \
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'
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.siblings[]
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| select(
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.rfilename != null
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and ($include_regex == "" or (.rfilename | test($include_regex)))
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and ($exclude_regex == "" or (.rfilename | test($exclude_regex) | not))
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)
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| [
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($endpoint + "/" + $repo_id + "/resolve/" + $revision + "/" + .rfilename),
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" dir=" + (.rfilename | split("/")[:-1] | join("/")),
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" out=" + (.rfilename | split("/")[-1]),
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if $token != "" then " header=Authorization: Bearer " + $token else empty end,
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""
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]
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| join("\n")
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'
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else
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printf "%s" "$RESPONSE" | jq -r \
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--arg endpoint "$HF_ENDPOINT" \
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--arg repo_id "$DOWNLOAD_API_PATH" \
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--arg include_regex "$INCLUDE_REGEX" \
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--arg exclude_regex "$EXCLUDE_REGEX" \
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--arg revision "$REVISION" \
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'
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.siblings[]
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| select(
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.rfilename != null
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and ($include_regex == "" or (.rfilename | test($include_regex)))
|
||||
and ($exclude_regex == "" or (.rfilename | test($exclude_regex) | not))
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)
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| ($endpoint + "/" + $repo_id + "/resolve/" + $revision + "/" + .rfilename)
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'
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fi
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}
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result=$(process_with_jq)
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printf "%s\n" "$result" > "$LOCAL_DIR/$fileslist_file"
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else
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printf "%b[Warning] jq not installed, using grep/awk for metadata json parsing (slower). Consider installing jq for better parsing performance.%b\n" "$YELLOW" "$NC"
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process_with_grep_awk() {
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local include_pattern=""
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local exclude_pattern=""
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local output=""
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|
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if ((${#INCLUDE_PATTERNS[@]})); then
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include_pattern=$(printf '%s\n' "${INCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
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||||
fi
|
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if ((${#EXCLUDE_PATTERNS[@]})); then
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exclude_pattern=$(printf '%s\n' "${EXCLUDE_PATTERNS[@]}" | sed 's/\./\\./g; s/\*/.*/g' | paste -sd '|' -)
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fi
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|
||||
local files=$(printf '%s' "$RESPONSE" | grep -o '"rfilename":"[^"]*"' | awk -F'"' '{print $4}')
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|
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if [[ -n "$include_pattern" ]]; then
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files=$(printf '%s\n' "$files" | grep -E "$include_pattern")
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||||
fi
|
||||
if [[ -n "$exclude_pattern" ]]; then
|
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files=$(printf '%s\n' "$files" | grep -vE "$exclude_pattern")
|
||||
fi
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while IFS= read -r file; do
|
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if [[ -n "$file" ]]; then
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if [[ "$TOOL" == "aria2c" ]]; then
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output+="$HF_ENDPOINT/$DOWNLOAD_API_PATH/resolve/$REVISION/$file"$'\n'
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output+=" dir=$(dirname "$file")"$'\n'
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output+=" out=$(basename "$file")"$'\n'
|
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[[ -n "$HF_TOKEN" ]] && output+=" header=Authorization: Bearer $HF_TOKEN"$'\n'
|
||||
output+=$'\n'
|
||||
else
|
||||
output+="$HF_ENDPOINT/$DOWNLOAD_API_PATH/resolve/$REVISION/$file"$'\n'
|
||||
fi
|
||||
fi
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||||
done <<< "$files"
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||||
printf '%s' "$output"
|
||||
}
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||||
|
||||
result=$(process_with_grep_awk)
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||||
printf "%s\n" "$result" > "$LOCAL_DIR/$fileslist_file"
|
||||
fi
|
||||
else
|
||||
printf "%bResume from file list: $LOCAL_DIR/$fileslist_file%b\n" "$GREEN" "$NC"
|
||||
fi
|
||||
|
||||
# Perform download
|
||||
printf "${YELLOW}Starting download with $TOOL to $LOCAL_DIR...\n${NC}"
|
||||
|
||||
cd "$LOCAL_DIR"
|
||||
if [[ "$TOOL" == "aria2c" ]]; then
|
||||
aria2c --console-log-level=error --file-allocation=none -x "$THREADS" -j "$CONCURRENT" -s "$THREADS" -k 1M -c -i "$fileslist_file" --save-session="$fileslist_file"
|
||||
elif [[ "$TOOL" == "wget" ]]; then
|
||||
wget -x -nH --cut-dirs="$CUT_DIRS" ${HF_TOKEN:+--header="Authorization: Bearer $HF_TOKEN"} --input-file="$fileslist_file" --continue
|
||||
fi
|
||||
|
||||
if [[ $? -eq 0 ]]; then
|
||||
printf "${GREEN}Download completed successfully. Repo directory: $PWD\n${NC}"
|
||||
else
|
||||
printf "${RED}Download encountered errors.\n${NC}"
|
||||
exit 1
|
||||
fi
|
||||
95
sentiment-analysis/test.py
Normal file
95
sentiment-analysis/test.py
Normal file
@@ -0,0 +1,95 @@
|
||||
import os
|
||||
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
|
||||
os.environ['HF_HOME'] = '/home/ht/huggingface/models'
|
||||
|
||||
from transformers import AutoTokenizer, AutoModelForSequenceClassification,TrainingArguments,Trainer
|
||||
from datasets import load_dataset
|
||||
|
||||
dataset_name = "imdb"
|
||||
task = "sentiment-analysis"
|
||||
dataset = load_dataset(dataset_name).shuffle()
|
||||
|
||||
|
||||
# 分割训练集和测试集
|
||||
train_dataset = dataset["train"].select(range(20000)) # 使用部分数据加速训练
|
||||
test_dataset = dataset["test"].select(range(5000))
|
||||
|
||||
model_name = "bert-base-cased"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
|
||||
def preprocess_function(examples):
|
||||
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
|
||||
|
||||
# 应用预处理
|
||||
tokenized_train = train_dataset.map(preprocess_function, batched=True)
|
||||
tokenized_test = test_dataset.map(preprocess_function, batched=True)
|
||||
|
||||
model = AutoModelForSequenceClassification.from_pretrained(
|
||||
"bert-base-cased",
|
||||
num_labels=2,
|
||||
id2label={0: "negative", 1: "positive"},
|
||||
label2id={"negative": 0, "positive": 1}
|
||||
)
|
||||
|
||||
# 设置训练参数
|
||||
training_args = TrainingArguments(
|
||||
output_dir="./results",
|
||||
eval_strategy="epoch",
|
||||
learning_rate=2e-5,
|
||||
per_device_train_batch_size=8,
|
||||
per_device_eval_batch_size=8,
|
||||
num_train_epochs= 10,
|
||||
weight_decay=0.01,
|
||||
save_strategy="epoch",
|
||||
load_best_model_at_end=True,
|
||||
)
|
||||
|
||||
|
||||
# 定义评估指标
|
||||
import numpy as np
|
||||
from sklearn.metrics import accuracy_score
|
||||
|
||||
def compute_metrics(eval_pred):
|
||||
logits, labels = eval_pred
|
||||
predictions = np.argmax(logits, axis=-1)
|
||||
return {"accuracy": accuracy_score(labels, predictions)}
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized_train,
|
||||
eval_dataset=tokenized_test,
|
||||
compute_metrics=compute_metrics,
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
|
||||
# 评估模型性能
|
||||
eval_results = trainer.evaluate()
|
||||
print(f"评估结果: {eval_results}")
|
||||
|
||||
# 保存微调后的模型
|
||||
model.save_pretrained("./fine_tuned_bert_imdb")
|
||||
tokenizer.save_pretrained("./fine_tuned_bert_imdb")
|
||||
|
||||
from transformers import pipeline
|
||||
|
||||
classifier = pipeline(
|
||||
"text-classification",
|
||||
model="./fine_tuned_bert_imdb",
|
||||
tokenizer="./fine_tuned_bert_imdb"
|
||||
)
|
||||
|
||||
# 示例预测
|
||||
print(classifier("This movie was fantastic! I loved every minute of it."))
|
||||
|
||||
|
||||
# data = dataset["train"]["text"][:10]
|
||||
# inputs = tokenizer(data, padding=True, truncation=True, return_tensors="pt")
|
||||
# outputs = model(**inputs)
|
||||
# predictions = outputs.logits.argmax(dim=-1)
|
||||
# labels = dataset["train"]["label"][:10]
|
||||
# for i ,(predictions,label) in enumerate(zip(predictions, labels)):
|
||||
# prediction_label = "positive" if predictions == 1 else "negative"
|
||||
# true_lable = "positive" if label == 1 else "negative"
|
||||
# print(f"Example {i+1}: Prediction: {prediction_label}, True Label: {true_lable}")
|
||||
Reference in New Issue
Block a user