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Eval Results (legacy)
Instructions to use SurweeshSP/mathtok with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SurweeshSP/mathtok with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SurweeshSP/mathtok")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SurweeshSP/mathtok", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SurweeshSP/mathtok with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SurweeshSP/mathtok" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SurweeshSP/mathtok", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SurweeshSP/mathtok
- SGLang
How to use SurweeshSP/mathtok with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "SurweeshSP/mathtok" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SurweeshSP/mathtok", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "SurweeshSP/mathtok" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SurweeshSP/mathtok", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SurweeshSP/mathtok with Docker Model Runner:
docker model run hf.co/SurweeshSP/mathtok
File size: 6,864 Bytes
edede4c | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | """
Tests for the Semantic Tokenizer Comparison Framework.
"""
import pytest
from evaluation.comparison import (
TokenizerStats, ComparisonRecord, TokenizerComparison,
_score_char, _score_gpt2, _score_mathtok,
_jaccard, _mean,
STANDARD_EXPRESSIONS, DEEP_NESTING_EXPRESSIONS, CANONICAL_PAIRS,
)
from mathtok.pipeline import MathTokPipeline
@pytest.fixture(scope="module")
def pipeline():
return MathTokPipeline(include_metadata=True)
@pytest.fixture(scope="module")
def comp(pipeline):
return TokenizerComparison(pipeline, gpt2_fn=None, save_jsonl=False)
# ββ TokenizerStats ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class TestTokenizerStats:
def test_scr_computed(self):
stats = TokenizerStats(
name="test", tokens=["OP_ADD", "VAR_X", "CONST_1"],
token_count=3,
operator_nodes=1, tree_depth=1,
parent_child_relations=1, function_scope=0,
canonical_bonus=2,
)
stats.compute_scr()
assert stats.structural_score == 5 # 1+1+1+0+2
assert abs(stats.raw_scr - 5/3) < 1e-9
assert abs(stats.structural_efficiency - 1/3) < 1e-9
def test_zero_token_count_safe(self):
stats = TokenizerStats(name="empty", tokens=[], token_count=0)
stats.compute_scr()
assert stats.raw_scr == 0.0
# ββ Character-level scorer βββββββββββββββββββββββββββββββββββββββββββββββββ
class TestCharScore:
def test_simple(self):
stats = _score_char("x + 1")
assert stats.token_count == 5
assert stats.operator_nodes >= 1 # at least +
assert stats.raw_scr >= 0
def test_nested_parens_depth(self):
stats = _score_char("sin((x+1)^2)")
assert stats.tree_depth >= 2 # at least 2 levels of parens
def test_no_function_scope(self):
# Character-level can't identify functions
stats = _score_char("sin(x)")
assert stats.function_scope == 0
# ββ GPT-2 heuristic scorer βββββββββββββββββββββββββββββββββββββββββββββββββ
class TestGPT2Score:
def test_operators_detected(self):
tokens = ["(", "x", "+", "1", ")", "^", "2"]
stats = _score_gpt2(tokens)
assert stats.operator_nodes >= 1
def test_function_detected(self):
tokens = ["sin", "(", "x", ")"]
stats = _score_gpt2(tokens)
assert stats.function_scope >= 1
def test_paren_depth(self):
tokens = ["(", "(", "x", ")", ")"]
stats = _score_gpt2(tokens)
assert stats.tree_depth == 2
def test_scr_positive(self):
tokens = ["sin", "(", "x", "^", "2", ")"]
stats = _score_gpt2(tokens)
stats.compute_scr()
assert stats.raw_scr >= 0
# ββ MathTok scorer ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class TestMathTokScore:
def test_add_expression(self, pipeline):
out = pipeline.encode_math_only("x + 1")
stats = _score_mathtok(out)
assert stats.token_count > 0
assert stats.operator_nodes >= 1 # OP_ADD
assert stats.canonical_bonus == 2 # successful parse
def test_function_expression(self, pipeline):
out = pipeline.encode_math_only("sin(x^2)")
stats = _score_mathtok(out)
assert stats.function_scope >= 1 # FUNC_SIN
def test_depth_nonzero(self, pipeline):
out = pipeline.encode_math_only("sin(x^2 + 1)")
stats = _score_mathtok(out)
assert stats.tree_depth >= 2
def test_scr_computed(self, pipeline):
out = pipeline.encode_math_only("(x+1)^2")
stats = _score_mathtok(out)
assert stats.raw_scr > 0
def test_mathtok_scr_higher_than_char(self, pipeline):
expr = "sin(x^2 + 1)"
out = pipeline.encode_math_only(expr)
mt = _score_mathtok(out)
ch = _score_char(expr)
# MathTok should have higher SCR due to semantic richness
assert mt.raw_scr > ch.raw_scr
# ββ Comparison mechanics ββββββββββββββββββββββββββββββββββββββββββββββββββ
class TestComparison:
def test_compare_one(self, comp):
rec = comp._compare_one("x + 1", "test")
assert isinstance(rec, ComparisonRecord)
assert rec.mathtok.token_count > 0
assert rec.char_level.token_count > 0
assert rec.gpt2 is None # no GPT-2 in fixture
def test_scr_improvement_vs_char(self, comp):
rec = comp._compare_one("sin(x^2)", "test")
# MathTok should outperform char-level on SCR
assert rec.scr_improvement_vs_char > 0
def test_canonical_jaccard(self, comp, pipeline):
# Equivalent expressions should have high Jaccard
out_a = pipeline.encode_math_only("x + 2")
out_b = pipeline.encode_math_only("2 + x")
mt_a = set(t for t in out_a.tokens if not t.startswith("["))
mt_b = set(t for t in out_b.tokens if not t.startswith("["))
jac = _jaccard(mt_a, mt_b)
assert jac > 0.5 # should be near 1.0 due to canonicalization
def test_run_standard_small(self, comp):
# Run just 3 expressions to keep test fast
for expr in STANDARD_EXPRESSIONS[:3]:
rec = comp._compare_one(expr, "standard")
assert rec.mathtok.token_count > 0
def test_deep_nesting_depth_increases(self, comp, pipeline):
flat = pipeline.encode_math_only("x + 1")
nested = pipeline.encode_math_only("sin(cos((x+1)^2))")
flat_d = max((m.depth for m in flat.metadata if m.depth >= 0), default=0)
nest_d = max((m.depth for m in nested.metadata if m.depth >= 0), default=0)
assert nest_d > flat_d
# ββ Utility helpers βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class TestHelpers:
def test_jaccard_identical(self):
assert _jaccard({"a", "b"}, {"a", "b"}) == 1.0
def test_jaccard_disjoint(self):
assert _jaccard({"a"}, {"b"}) == 0.0
def test_jaccard_partial(self):
j = _jaccard({"a", "b"}, {"b", "c"})
assert abs(j - 1/3) < 1e-9
def test_mean_empty(self):
assert _mean([]) == 0.0
def test_mean_values(self):
assert abs(_mean([1.0, 2.0, 3.0]) - 2.0) < 1e-9
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