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Documentation: update cross_encoder_finetuning.ipynb (run-llama#12383)
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eltociear authored Mar 29, 2024
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" 1. For the current row being iterated, create a vector index using the paper document provided in the paper column of the dataset\n",
" 2. Query the vector index with a top_k value of top 3 nodes without any reranker\n",
" 3. Compare the generated answers with the reference answers of the respective sample using Pairwise Comparison Evaluator and add the scores to a list\n",
"5. Repeat 1 untill all the rows have been iterated\n",
"5. Repeat 1 until all the rows have been iterated\n",
"6. Calculate avg scores over all samples/ rows\n"
]
},
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" 2. Query the vector index with a top_k value of top 5 nodes.\n",
" 3. Use cross-encoder/ms-marco-MiniLM-L-12-v2 as a reranker as a NodePostprocessor to get top_k value of top 3 nodes out of the 8 nodes\n",
" 3. Compare the generated answers with the reference answers of the respective sample using Pairwise Comparison Evaluator and add the scores to a list\n",
"5. Repeat 1 untill all the rows have been iterated\n",
"5. Repeat 1 until all the rows have been iterated\n",
"6. Calculate avg scores over all samples/ rows\n"
]
},
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" 2. Query the vector index with a top_k value of top 5 nodes.\n",
" 3. Use finetuned version of cross-encoder/ms-marco-MiniLM-L-12-v2 saved as bpHigh/Cross-Encoder-LLamaIndex-Demo as a reranker as a NodePostprocessor to get top_k value of top 3 nodes out of the 8 nodes\n",
" 3. Compare the generated answers with the reference answers of the respective sample using Pairwise Comparison Evaluator and add the scores to a list\n",
"5. Repeat 1 untill all the rows have been iterated\n",
"5. Repeat 1 until all the rows have been iterated\n",
"6. Calculate avg scores over all samples/ rows"
]
},
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