Question: What transfer learning tasks are evaluated?

Evidence:
raw_evidence: We compare the SBERT sentence embeddings to other sentence embeddings methods on the following seven SentEval transfer tasks:
MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25.
CR: Sentiment prediction of customer product reviews BIBREF26.
SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27.
MPQA: Phrase level opinion polarity classification from newswire BIBREF28.
SST: Stanford Sentiment Treebank with binary labels BIBREF29.
TREC: Fine grained question-type classification from TREC BIBREF30.
MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31.
raw_evidence: The purpose of SBERT sentence embeddings are not to be used for transfer learning for other tasks. Here, we think fine-tuning BERT as described by devlin2018bert for new tasks is the more suitable method, as it updates all layers of the BERT network. However, SentEval can still give an impression on the quality of our sentence embeddings for various tasks.
We compare the SBERT sentence embeddings to other sentence embeddings methods on the following seven SentEval transfer tasks:
MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25.
CR: Sentiment prediction of customer product reviews BIBREF26.
SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27.
MPQA: Phrase level opinion polarity classification from newswire BIBREF28.
SST: Stanford Sentiment Treebank with binary labels BIBREF29.
TREC: Fine grained question-type classification from TREC BIBREF30.
MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31.
raw_evidence: We fine-tune SBERT on NLI data, which creates sentence embeddings that significantly outperform other state-of-the-art sentence embedding methods like InferSent BIBREF4 and Universal Sentence Encoder BIBREF5. On seven Semantic Textual Similarity (STS) tasks, SBERT achieves an improvement of 11.7 points compared to InferSent and 5.5 points compared to Universal Sentence Encoder. On SentEval BIBREF6, an evaluation toolkit for sentence embeddings, we achieve an improvement of 2.1 and 2.6 points, respectively.
We compare the SBERT sentence embeddings to other sentence embeddings methods on the following seven SentEval transfer tasks:
MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25.
CR: Sentiment prediction of customer product reviews BIBREF26.
SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27.
MPQA: Phrase level opinion polarity classification from newswire BIBREF28.
SST: Stanford Sentiment Treebank with binary labels BIBREF29.
TREC: Fine grained question-type classification from TREC BIBREF30.
MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31.

Answers:
MR, CR, SUBJ, MPQA, SST, TREC, MRPC
MR: Sentiment prediction for movie reviews snippets on a five start scale BIBREF25.

CR: Sentiment prediction of customer product reviews BIBREF26.

SUBJ: Subjectivity prediction of sentences from movie reviews and plot summaries BIBREF27.

MPQA: Phrase level opinion polarity classification from newswire BIBREF28.

SST: Stanford Sentiment Treebank with binary labels BIBREF29.

TREC: Fine grained question-type classification from TREC BIBREF30.

MRPC: Microsoft Research Paraphrase Corpus from parallel news sources BIBREF31.
Semantic Textual Similarity, sentiment prediction, subjectivity prediction, phrase level opinion polarity classification, Stanford Sentiment Treebank, fine grained question-type classification.