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The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can safely remove it from your `config.json` file.
The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can safely remove it from your `config.json` file.
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The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can safely remove it from your `config.json` file.
The `xla_device` argument has been deprecated in v4.4.0 of Transformers. It is ignored and you can safely remove it from your `config.json` file.
from transformers import TFAutoModel, AutoTokenizer
def combine_results(passages, k=4):
passages_list = list(divide_chunks(passages, k))
passages_str = [" ".join(p) for p in passages_list]
return passages_str
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All model checkpoint layers were used when initializing TFBertModel.
All the layers of TFBertModel were initialized from the model checkpoint at nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.
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All model checkpoint layers were used when initializing TFBertModel.
All the layers of TFBertModel were initialized from the model checkpoint at nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.
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def get_answer_full_sent(m_passages, answer_dict):
all_sents = list(nlp(m_passages).sents)
all_sents = [s.text for s in all_sents]
for i in range(len(all_sents)):
if len("".join(all_sents[0:i])[answer_dict['start']:answer_dict['end']])>2:
answer_dict['answer_sentence'] = all_sents[i-1]
return answer_dict
return answer_dict
from sentence_transformers import CrossEncoder
ranking_model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', max_length=196)
def get_reranked_passage(passages, question, top_rr):
passage_question_pair = [(question, p) for p in passages]
scores = ranking_model.predict(passage_question_pair)
shorted_index = np.argpartition(scores, -top_rr)[::-1]
shorted_scores = np.array([scores[i] for i in shorted_index])
return [passages[i] for i in shorted_index[0:top_rr]]
# end to end with dpr
topk_r=30
topk_rr=8
import pandas as pd
def get_answer_dpr(question):
passages = get_passage_for_question(question, debug=True)
print("Total passages: ", len(passages))
passage_embeddings = extracted_passage_embeddings(passages)
query_embeddings = extracted_query_embeddings([question])
faiss_index = faiss.IndexFlatL2(128)
faiss_index.add(passage_embeddings.pooler_output)
prob, index = faiss_index.search(query_embeddings.pooler_output, k=topk_r)
r_passages = [passages[i] for i in index[0]]
print("Top k retrieved passages :", len(r_passages))
rr_passages = get_reranked_passage(r_passages, question, topk_rr)
print("Top k reranked passages :", len(rr_passages))
m_passages = combine_results(rr_passages)
print("Merged passages :", len(m_passages))
results = qa(question=[question]*len(m_passages), context=m_passages, max_seq_len=512)
if isinstance(results, dict):
results = [results]
output_results = [get_answer_full_sent(m_passages[i],results[i]) for i in range(len(results))]
return pd.DataFrame(output_results)[['answer', 'answer_sentence', 'score']].sort_values("score", ascending=False)
results= get_answer_dpr("where was tara located in gone with the wind?")
results
Top Wiki hits : ['Tara (plantation)', 'Margaret Mitchell', 'RKO Forty Acres']
Total passages: 95
1/1 [==============================] - 0s 470ms/step
1/1 [==============================] - 0s 21ms/step
Top k retrieved passages : 30
Top k reranked passages : 8
Merged passages : 2
/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
return array(a, dtype, copy=False, order=order)
import gradio as gr
inp = gr.inputs.Textbox(lines=2, default='what is coronavirus?', label="Question")
out = gr.outputs.Dataframe(label="Answers")#gr.outputs.Textbox(label="Answers")
gr.Interface(fn=get_answer_dpr, inputs=inp, outputs=out).launch()
Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`
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