---
name: transformers-inference-27543c27e543
description: "HuggingFace Transformers for model inference. Use when: text classification, NER, question answering, summarization, embeddings, zero-shot classification. NOT for: training large models (use cloud), simple regex/rule-based tasks, production serving at scale (use vLLM)."
metadata: { "openclaw": { "emoji": "🤗", "requires": { "bins": ["python3"] }, "install": [{ "id": "uv-transformers", "kind": "uv", "package": "transformers" }, { "id": "uv-torch", "kind": "uv", "package": "torch" }, { "id": "uv-sentencepiece", "kind": "uv", "package": "sentencepiece" }] } }
---

# HuggingFace Transformers Inference

Text classification, NER, question answering, summarization, embeddings, and zero-shot classification using pretrained models.

## When to Use / When NOT to Use

**Use when:** text classification, named entity recognition, question answering, summarization, text generation, sentence embeddings, zero-shot classification, translation, fill-mask tasks.

**NOT for:** training large models from scratch (use cloud GPU clusters), simple regex or rule-based text processing, production serving at scale (use vLLM or TGI), tasks that don't need neural models.

## Quick Pipelines

```python
from transformers import pipeline

# Text classification (sentiment)
clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
result = clf("This movie was fantastic!")
# [{'label': 'POSITIVE', 'score': 0.9998}]

# Named entity recognition
ner = pipeline("ner", model="dslim/bert-base-NER", aggregation_strategy="simple")
entities = ner("Hugging Face is based in New York City.")
# [{'entity_group': 'ORG', ...}, {'entity_group': 'LOC', ...}]

# Question answering (extractive)
qa = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
answer = qa(question="What is the capital of France?",
            context="France is a country in Europe. Its capital is Paris.")
# {'answer': 'Paris', 'score': 0.99, ...}

# Summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(long_text, max_length=130, min_length=30, do_sample=False)

# Zero-shot classification (no task-specific training needed)
zsc = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
result = zsc("I need to pay my electricity bill",
             candidate_labels=["finance", "health", "technology"])
# {'labels': ['finance', ...], 'scores': [0.95, ...]}
```

## Manual Model and Tokenizer Loading

```python
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
import torch

model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

inputs = tokenizer("Hello world", return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)

# outputs.last_hidden_state: [batch, seq_len, hidden_dim]
# outputs.pooler_output:     [batch, hidden_dim]
```

## Sentence Embeddings (Mean Pooling)

```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F

model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

def get_embeddings(texts):
    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
    mask = inputs['attention_mask'].unsqueeze(-1)
    embeddings = (outputs.last_hidden_state * mask).sum(1) / mask.sum(1)
    return F.normalize(embeddings, p=2, dim=1)

embs = get_embeddings(["Hello world", "Hi there"])
similarity = torch.mm(embs, embs.T)  # cosine similarity matrix
```

## Batch Processing

```python
clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english",
               device=0 if torch.cuda.is_available() else -1)

texts = ["Great product!", "Terrible service.", "It was okay."]
results = clf(texts, batch_size=16)           # batch for throughput

# For large datasets, use dataset streaming
from transformers import pipeline
for result in clf(iter(large_text_list), batch_size=32):
    process(result)
```

## Best Practices

1. Use `pipeline()` for quick prototyping; load model/tokenizer manually for custom logic.
2. Set `device=0` for GPU or `device="mps"` on Apple Silicon; default is CPU.
3. Always use `torch.no_grad()` during inference to save memory.
4. Use `truncation=True` and `max_length` to handle long inputs safely.
5. For repeated inference, load the model once and reuse; avoid reloading per call.
6. Prefer distilled models (distilbert, distilgpt2) for faster inference when accuracy allows.
7. Use `model.half()` or `torch.float16` to reduce memory footprint on GPU.
8. Cache models locally with `TRANSFORMERS_CACHE` env var to avoid repeated downloads.


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/transformers-inference/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/transformers-inference/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: API
Tools: Huggingface, Pytorch
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 3e0d4c117e02e53e3d4055a4279429da8fa58f2634e3086c51d6626180b809f8