Huggingface Inference GuideSAFE

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---
name: huggingface-inference-guide
description: "Run NLP and CV model inference via Hugging Face free-tier API"
metadata:
  openclaw:
    emoji: "🤗"
    category: "domains"
    subcategory: "ai-ml"
    keywords: ["huggingface", "inference", "nlp", "machine-learning", "transformers", "models"]
    source: "https://huggingface.co/docs/api-inference/index"
---

# Hugging Face Inference API Guide

## Overview

The Hugging Face Inference API provides instant access to thousands of pre-trained machine learning models for natural language processing, computer vision, audio processing, and multimodal tasks. Researchers can run inference on state-of-the-art models without managing infrastructure, GPU resources, or complex deployment pipelines.

The API hosts models from the Hugging Face Hub, which contains over 500,000 models contributed by the research community. This includes transformer models for text classification, named entity recognition, summarization, translation, question answering, text generation, and image classification. For academic researchers, the Inference API is invaluable for rapid prototyping, benchmark evaluation, and integrating ML capabilities into research workflows without dedicated compute resources.

The free tier provides access to a broad selection of models with rate limits suitable for development and small-scale research. An API token is required for authentication, available for free at huggingface.co.

## Authentication

A free Hugging Face API token is required. Create an account and generate a token at https://huggingface.co/settings/tokens.

Store your token securely in an environment variable:

```bash
export HF_API_TOKEN=$HF_API_TOKEN
```

```bash
curl -X POST "https://api-inference.huggingface.co/models/bert-base-uncased" \
  -H "Authorization: Bearer $HF_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "The goal of life is [MASK]."}'
```

## Core Endpoints

### Text Classification (Sentiment Analysis)

```
POST https://api-inference.huggingface.co/models/{model_id}
```

```bash
curl -s -X POST \
  "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english" \
  -H "Authorization: Bearer $HF_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "This research methodology provides robust and reproducible results."}' \
  | python3 -m json.tool
```

### Named Entity Recognition

```bash
curl -s -X POST \
  "https://api-inference.huggingface.co/models/dslim/bert-base-NER" \
  -H "Authorization: Bearer $HF_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "Dr. Marie Curie conducted research at the University of Paris on radioactivity."}' \
  | python3 -m json.tool
```

### Text Summarization

```bash
curl -s -X POST \
  "https://api-inference.huggingface.co/models/facebook/bart-large-cnn" \
  -H "Authorization: Bearer $HF_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "The study of quantum computing has seen tremendous advances in the past decade. Researchers have demonstrated quantum supremacy with processors containing over 100 qubits. Error correction remains a significant challenge, but recent breakthroughs in topological qubits and surface codes suggest viable paths forward. Applications in drug discovery, materials science, and cryptography are expected to be among the first practical use cases.",
    "parameters": {"max_length": 80, "min_length": 30}
  }' | python3 -m json.tool
```

### Zero-Shot Classification

Classify text into arbitrary categories without fine-tuning.

```bash
curl -s -X POST \
  "https://api-inference.huggingface.co/models/facebook/bart-large-mnli" \
  -H "Authorization: Bearer $HF_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "New CRISPR technique enables precise gene editing in human stem cells",
    "parameters": {"candidate_labels": ["biology", "computer science", "physics", "economics"]}
  }' | python3 -m json.tool
```

### Python Example: Batch Sentiment Analysis of Paper Abstracts

```python
import requests
import os
import time

API_URL = "https://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english"
HEADERS = {"Authorization": f"Bearer {os.environ['HF_API_TOKEN']}"}

def classify_sentiment(texts):
    """Classify sentiment for a batch of texts."""
    response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts})
    if response.status_code == 503:
        # Model is loading, wait and retry
        wait_time = response.json().get("estimated_time", 20)
        print(f"Model loading, waiting {wait_time:.0f}s...")
        time.sleep(wait_time)
        response = requests.post(API_URL, headers=HEADERS, json={"inputs": texts})
    response.raise_for_status()
    return response.json()

abstracts = [
    "Our results demonstrate a significant improvement over baseline methods.",
    "The proposed approach failed to achieve meaningful gains on the benchmark.",
    "We present preliminary findings that warrant further investigation.",
]

results = classify_sentiment(abstracts)
for abstract, result in zip(abstracts, results):
    top = max(result, key=lambda x: x["score"])
    print(f"Sentiment: {top['label']} ({top['score']:.3f})")
    print(f"  Text: {abstract[:80]}...")
    print()
```

### Python Example: Research Paper Topic Classification

```python
import requests
import os

ZSC_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-mnli"
HEADERS = {"Authorization": f"Bearer {os.environ['HF_API_TOKEN']}"}

def classify_paper(abstract, categories):
    """Classify a paper abstract into research categories."""
    payload = {
        "inputs": abstract,
        "parameters": {"candidate_labels": categories}
    }
    resp = requests.post(ZSC_URL, headers=HEADERS, json=payload)
    resp.raise_for_status()
    return resp.json()

categories = [
    "machine learning",
    "computational biology",
    "natural language proce

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