Unverified50% confidenceFactExact time
The token-level F1 score separately computes the token-level precision and recall between the predicted sequence and the ground-truth sequence and then takes the harmonic mean, but it cannot distinguish semantically equivalent names with different spellings.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
asmsense: 228K x86-64 Function Dataset Tackles Reverse Engineering Annotation Challenges
redditr/MachineLearning10/18/2026
Related Entities
Related Claims
VerifiedThe F1 score is the harmonic mean of recall and precision.66% similarVerifiedMacro F1 computes F1 independently for each class then takes a simple arithmetic average across all classes63% similarUnverifiedCriticGen's F1 scores on criteria-based rationales and actionable suggestions improved from 0.6369/0.5994 to 0.7554/0.7900, respectively.61% similarUnverifiedIn autoregressive decoding, the probability of each token is conditioned on all preceding tokens, with the mathematical form P(x_t | x_1, ..., x_{t-1}).60% similarUnverifiedGLM-5.3's thinking tokens count as output tokens and are billed at the 24 multiplier, while the model uses its highest reasoning effort by default.59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1061126API
curl https://kongchang.com/api/v1/knowledge/claims/1061126MCP
get_claim(id=1061126)