Instructions to use Meanblock/JEV-CPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meanblock/JEV-CPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Meanblock/JEV-CPU")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Meanblock/JEV-CPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,383 Bytes
7845694 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import pytest
from semif_phase1.reranker import _answer_ids, _encode
class Tokenizer:
def encode(self, text, add_special_tokens=False):
assert add_special_tokens is False
return list(text.encode())
class AnswerTokenizer:
def encode(self, text, add_special_tokens=False):
return {"no": [4], "yes": [7]}[text]
def convert_tokens_to_ids(self, text):
return {"no": 4, "yes": 7}[text]
def test_reranker_pair_contains_only_declared_semantics():
row = {
"id": "case",
"state": "STATE",
"question": "QUESTION",
"options": [{"id": "a", "description": "ANSWER"}, {"id": "b", "description": "OTHER"}],
"label": "b",
}
ids, prompt_hash = _encode(Tokenizer(), row, row["options"][0], 4096)
prompt = bytes(ids).decode()
assert all(value in prompt for value in ("STATE", "QUESTION", "ANSWER"))
assert "label" not in prompt and row["options"][1]["description"] not in prompt
assert len(prompt_hash) == 64
def test_no_silent_truncation():
row = {"id": "case", "state": "x" * 100, "question": "q", "options": [{"id": "a", "description": "a"}]}
with pytest.raises(ValueError, match="no truncation|exceed"):
_encode(Tokenizer(), row, row["options"][0], 10)
def test_official_yes_no_token_contract_is_checked():
assert _answer_ids(AnswerTokenizer()) == (4, 7)
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