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Update _nv_metrics.py #2053

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30 changes: 15 additions & 15 deletions src/ragas/metrics/_nv_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,17 +21,17 @@ class AnswerAccuracy(MetricWithLLM, SingleTurnMetric):
This metric averages two distinct judge prompts to evaluate.

Top10, Zero-shoot LLM-as-a-Judge Leaderboard:
1)- mistralai/mixtral-8x22b-instruct-v0.1
2)- mistralai/mixtral-8x7b-instruct-v0.1
3)- meta/llama-3.1-70b-instruct
4)- meta/llama-3.3-70b-instruct
5)- meta/llama-3.1-405b-instruct
6)- mistralai/mistral-nemo-12b-instruct
7)- nvidia/llama-3.1-nemotron-70b-instruct
8)- meta/llama-3.1-8b-instruct
9)- google/gemma-2-2b-it
10)- nvidia/nemotron-mini-4b-instruct
The top1 LB model have high correlation with human judges (~0.90).
1)- nvidia/Llama-3_3-Nemotron-Super-49B-v1
2)- mistralai/mixtral-8x22b-instruct-v0.1
3)- mistralai/mixtral-8x7b-instruct-v0.1
4)- meta/llama-3.1-70b-instruct
5)- meta/llama-3.3-70b-instruct
6)- meta/llama-3.1-405b-instruct
7)- mistralai/mistral-nemo-12b-instruct
8)- nvidia/llama-3.1-nemotron-70b-instruct
9)- meta/llama-3.1-8b-instruct
10)- google/gemma-2-2b-it
The top1 LB model have high correlation with human judges (~0.92).

Attributes
----------
Expand Down Expand Up @@ -252,7 +252,7 @@ async def _single_turn_ascore(
formatted_prompt = StringPromptValue(
text=self.template_relevance1.format(
query=sample.user_input,
context="\n".join(sample.retrieved_contexts)[:7000],
context="\n".join(sample.retrieved_contexts),
)
)
req = self.llm.agenerate_text(
Expand All @@ -271,7 +271,7 @@ async def _single_turn_ascore(
formatted_prompt = StringPromptValue(
text=self.template_relevance1.format(
query=sample.user_input,
context="\n".join(sample.retrieved_contexts)[:7000],
context="\n".join(sample.retrieved_contexts),
)
)
req = self.llm.agenerate_text(
Expand Down Expand Up @@ -385,7 +385,7 @@ async def _single_turn_ascore(
for retry in range(self.retry):
formatted_prompt = StringPromptValue(
text=self.template_groundedness1.format(
context="\n".join(sample.retrieved_contexts)[:7000],
context="\n".join(sample.retrieved_contexts),
response=sample.response,
)
)
Expand All @@ -404,7 +404,7 @@ async def _single_turn_ascore(
for retry in range(self.retry):
formatted_prompt = StringPromptValue(
text=self.template_groundedness2.format(
context="\n".join(sample.retrieved_contexts)[:7000],
context="\n".join(sample.retrieved_contexts),
response=sample.response,
)
)
Expand Down
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