babel_machine / interfaces /cap_minor.py
kovacsvi
npc
9601263
import gradio as gr
import os
import torch
import numpy as np
import pandas as pd
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import torch.nn.functional as F
from huggingface_hub import HfApi
from collections import defaultdict
from label_dicts import (
CAP_NUM_DICT,
CAP_LABEL_NAMES,
CAP_MIN_NUM_DICT,
CAP_MIN_LABEL_NAMES,
)
from .utils import is_disk_full, release_model
HF_TOKEN = os.environ["hf_read"]
languages = [
"Multilingual",
]
domains = {
"media": "media",
"social media": "social",
"parliamentary speech": "parlspeech",
"legislative documents": "legislative",
"executive speech": "execspeech",
"executive order": "execorder",
"party programs": "party",
"judiciary": "judiciary",
"budget": "budget",
"public opinion": "publicopinion",
"local government agenda": "localgovernment",
}
CAP_MAJOR_CODES = list(CAP_NUM_DICT.values())
CAP_MIN_CODES = list(CAP_MIN_NUM_DICT.values())
major_index_to_id = {i: code for i, code in enumerate(CAP_MAJOR_CODES)}
minor_id_to_index = {code: i for i, code in enumerate(CAP_MIN_CODES)}
minor_index_to_id = {i: code for i, code in enumerate(CAP_MIN_CODES)}
major_to_minor_map = defaultdict(list)
for code in CAP_MIN_CODES:
major_id = int(str(code)[:-2])
major_to_minor_map[major_id].append(code)
major_to_minor_map = dict(major_to_minor_map)
def normalize_probs(probs: dict) -> dict:
total = sum(probs.values())
return {k: v / total for k, v in probs.items()}
def check_huggingface_path(checkpoint_path: str):
try:
hf_api = HfApi(token=HF_TOKEN)
hf_api.model_info(checkpoint_path, token=HF_TOKEN)
return True
except:
return False
def build_huggingface_path(language: str, domain: str, hierarchical: bool):
if hierarchical:
major = "poltextlab/xlm-roberta-large-pooled-cap-v3"
minor = (
"poltextlab/xlm-roberta-large-twitter-cap-minor"
if domain == "social"
else "poltextlab/xlm-roberta-large-pooled-cap-minor-v3"
)
return major, minor
return (
"poltextlab/xlm-roberta-large-twitter-cap-minor"
if domain == "social"
else "poltextlab/xlm-roberta-large-pooled-cap-minor-v3"
)
def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
device = torch.device("cpu")
# Load major and minor models + tokenizer
major_model = AutoModelForSequenceClassification.from_pretrained(
major_model_id,
low_cpu_mem_usage=True,
device_map="auto",
offload_folder="offload",
token=HF_TOKEN,
).to(device)
minor_model = AutoModelForSequenceClassification.from_pretrained(
minor_model_id,
low_cpu_mem_usage=True,
device_map="auto",
offload_folder="offload",
token=HF_TOKEN,
).to(device)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
# Tokenize input
inputs = tokenizer(
text, max_length=64, truncation=True, padding=True, return_tensors="pt"
).to(device)
# Predict major topic
major_model.eval()
with torch.no_grad():
major_logits = major_model(**inputs).logits
major_probs = F.softmax(major_logits, dim=-1)
major_probs_np = major_probs.cpu().numpy().flatten()
top_major_index = int(np.argmax(major_probs_np))
top_major_id = major_index_to_id[top_major_index]
# Default: show major topic predictions
if int(top_major_id) == 999:
filtered_probs = {21:1}
else:
filtered_probs = {
i: float(major_probs_np[i]) for i in np.argsort(major_probs_np)[::-1]
}
filtered_probs = normalize_probs(filtered_probs)
output_pred = {
f"[{major_index_to_id[k]}] {CAP_LABEL_NAMES[major_index_to_id[k]]}": v
for k, v in sorted(
filtered_probs.items(), key=lambda item: item[1], reverse=True
)
}
print(top_major_id) # debug
print(major_to_minor_map) # debug
# If eligible for minor prediction
if top_major_id in major_to_minor_map:
valid_minor_ids = major_to_minor_map[top_major_id]
minor_model.eval()
with torch.no_grad():
minor_logits = minor_model(**inputs).logits
minor_probs = F.softmax(minor_logits, dim=-1)
release_model(major_model, major_model_id)
release_model(minor_model, minor_model_id)
print(minor_probs) # debug
# Restrict to valid minor codes
valid_indices = [
minor_id_to_index[mid]
for mid in valid_minor_ids
if mid in minor_id_to_index
]
filtered_probs = {
minor_index_to_id[i]: float(minor_probs[0][i]) for i in valid_indices
}
print(filtered_probs) # debug
filtered_probs = normalize_probs(filtered_probs)
print(filtered_probs) # debug
output_pred = {
f"[{top_major_id}] {CAP_LABEL_NAMES[top_major_id]} [{k}] {CAP_MIN_LABEL_NAMES[k]}": v
for k, v in sorted(
filtered_probs.items(), key=lambda item: item[1], reverse=True
)
}
output_info = f'<p style="text-align: center; display: block">Prediction used <a href="https://huggingface.co/{major_model_id}">{major_model_id}</a> and <a href="https://huggingface.co/{minor_model_id}">{minor_model_id}</a>.</p>'
interpretation_info = """
## How to Interpret These Values (Hierarchical Classification)
The values are the confidences for minor topics **within a given major topic**, and they are **normalized to sum to 1**.
"""
return interpretation_info, output_pred, output_info
def predict_flat(text, model_id, tokenizer_id, HF_TOKEN=None):
device = torch.device("cpu")
# Load JIT-traced model
jit_model_path = f"/data/jit_models/{model_id.replace('/', '_')}.pt"
model = torch.jit.load(jit_model_path).to(device)
model.eval()
# Load tokenizer (still regular HF)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
# Tokenize input
inputs = tokenizer(
text, max_length=64, truncation=True, padding=True, return_tensors="pt"
)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
output = model(inputs["input_ids"], inputs["attention_mask"])
print(output) # debug
logits = output["logits"]
release_model(model, model_id)
probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
top_indices = np.argsort(probs)[::-1][:10]
output_pred = {}
for i in top_indices:
code = CAP_MIN_NUM_DICT[i]
prob = probs[i]
if code in CAP_LABEL_NAMES:
# Media (major) topic
label = CAP_LABEL_NAMES[code]
display = f"[{code}] {label}"
else:
# Minor topic
major_code = code // 100
major_label = CAP_LABEL_NAMES[major_code]
minor_label = CAP_MIN_LABEL_NAMES[code]
display = f"[{major_code}] {major_label} [{code}] {minor_label}"
output_pred[display] = prob
interpretation_info = """
## How to Interpret These Values (Flat Classification)
This method returns predictions made by a single model. **Only the top 10 most confident labels are displayed**.
"""
output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
return interpretation_info, output_pred, output_info
def predict_cap(tmp, method, text, language, domain):
if is_disk_full():
os.system("rm -rf /data/models*")
os.system("rm -r ~/.cache/huggingface/hub")
domain = domains[domain]
if method == "Hierarchical Classification":
major_model_id, minor_model_id = build_huggingface_path(language, domain, True)
tokenizer_id = "xlm-roberta-large"
return predict(text, major_model_id, minor_model_id, tokenizer_id)
else:
model_id = build_huggingface_path(language, domain, False)
tokenizer_id = "xlm-roberta-large"
return predict_flat(text, model_id, tokenizer_id)
description = """
You can choose between two approaches for making predictions:
**1. Hierarchical Classification**
First, the model predicts a **major topic**. Then, a second model selects the most probable **subtopic** from within that major topic's category.
**2. Flat Classification (single model)**
A single model directly predicts the most relevant label from all available minor topics.
"""
demo = gr.Interface(
title="CAP Minor Topics Babel Demo",
fn=predict_cap,
inputs=[
gr.Markdown(description),
gr.Radio(
choices=["Hierarchical Classification", "Flat Classification"],
label="Prediction Mode",
value="Hierarchical Classification",
),
gr.Textbox(lines=6, label="Input"),
gr.Dropdown(languages, label="Language", value=languages[0]),
gr.Dropdown(domains.keys(), label="Domain", value=list(domains.keys())[0]),
],
outputs=[gr.Markdown(), gr.Label(label="Output"), gr.Markdown()],
)