ACL anthology corpus
1.0.0
Repositori ini menyediakan teks lengkap dan metadata ke koleksi antologi ACL (80 ribu artikel/poster per September 2022) juga termasuk file .pdf dan ekstraksi grobid dari pdf.
Data sekarang dihosting di huggingface! Silakan unduh dari sana. Ini adalah yang paling mutakhir. https://huggingface.co/datasets/ACL-OCL/acl-anthology-corpus
Tujuannya adalah untuk terus memperbarui korpus ini dan menyediakan repositori komprehensif dari koleksi ACL lengkap.
Repositori ini menyediakan data untuk 80,013 artikel/poster ACL -
| Nama kolom | Keterangan |
|---|---|
acl_id | ID ACL unik |
abstract | abstrak diekstraksi oleh GROBID |
full_text | teks lengkap diekstraksi oleh GROBID |
corpus_paper_id | ID Cendekiawan Semantik |
pdf_hash | sha1 hash dari pdf |
numcitedby | jumlah sitasi dari S2 |
url | tautan publikasi |
publisher | - |
address | Alamat konferensi |
year | - |
month | - |
booktitle | - |
author | daftar penulis |
title | judul makalah |
pages | - |
doi | - |
number | - |
volume | - |
journal | - |
editor | - |
isbn | - |
>> > import pandas as pd
>> > df = pd . read_parquet ( 'acl-publication-info.74k.parquet' )
>> > df
acl_id abstract full_text corpus_paper_id pdf_hash ... number volume journal editor isbn
0 O02 - 2002 There is a need to measure word similarity whe ... There is a need to measure word similarity whe ... 18022704 0b0 9178 ac8d17a92f16140365363d8df88c757d0 ... None None None None None
1 L02 - 1310 8220988 8 d5e31610bc82c2abc86bc20ceba684c97e66024 ... None None None None None
2 R13 - 1042 Thread disentanglement is the task of separati ... Thread disentanglement is the task of separati ... 16703040 3 eb736b17a5acb583b9a9bd99837427753632cdb ... None None None None None
3 W05 - 0819 In this paper , we describe a word alignment al ... In this paper , we describe a word alignment al ... 1215281 b20450f67116e59d1348fc472cfc09f96e348f55 ... None None None None None
4 L02 - 1309 18078432 011e943 b64a78dadc3440674419821ee080f0de3 ... None None None None None
... ... ... ... ... ... ... ... ... ... ... ...
73280 P99 - 1002 This paper describes recent progress and the a ... This paper describes recent progress and the a ... 715160 ab17a01f142124744c6ae425f8a23011366ec3ee ... None None None None None
73281 P00 - 1009 We present an LFG - DOP parser which uses fragme ... We present an LFG - DOP parser which uses fragme ... 1356246 ad005b3fd0c867667118482227e31d9378229751 ... None None None None None
73282 P99 - 1056 The processes through which readers evoke ment ... The processes through which readers evoke ment ... 7277828 924 cf7a4836ebfc20ee094c30e61b949be049fb6 ... None None None None None
73283 P99 - 1051 This paper examines the extent to which verb d ... This paper examines the extent to which verb d ... 1829043 6 b1f6f28ee36de69e8afac39461ee1158cd4d49a ... None None None None None
73284 P00 - 1013 Spoken dialogue managers have benefited from u ... Spoken dialogue managers have benefited from u ... 10903652 483 c818c09e39d9da47103fbf2da8aaa7acacf01 ... None None None None None
[ 73285 rows x 21 columns ]Id ACL yang diberikan juga konsisten dengan S2 API -
https://api.semanticscholar.org/graph/v1/paper/ACL:P83-1025
API dapat digunakan untuk mengambil lebih banyak informasi untuk setiap makalah di korpus.
Kami menyempurnakan model distilgpt2 dari huggingface menggunakan teks lengkap dari korpus ini. Model dilatih untuk tugas pembangkitan.
Demo Pembuatan Teks: https://huggingface.co/shaurya0512/distilgpt2-finetune-acl22
Contoh:
>> > from transformers import AutoTokenizer , AutoModelForCausalLM
>> > tokenizer = AutoTokenizer . from_pretrained ( "shaurya0512/distilgpt2-finetune-acl22" )
>> > model = AutoModelForCausalLM . from_pretrained ( "shaurya0512/distilgpt2-finetune-acl22" )
>> >
>> > input_context = "We introduce a new language representation"
>> > input_ids = tokenizer . encode ( input_context , return_tensors = "pt" ) # encode input context
>> > outputs = model . generate (
... input_ids = input_ids , max_length = 128 , temperature = 0.7 , repetition_penalty = 1.2
... ) # generate sequences
>> > print ( f"Generated: { tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True ) } " ) Generated: We introduce a new language representation for the task of sentiment classification. We propose an approach to learn representations from
unlabeled data, which is based on supervised learning and can be applied in many applications such as machine translation (MT) or information retrieval
systems where labeled text has been used by humans with limited training time but no supervision available at all. Our method achieves state-oftheart
results using only one dataset per domain compared to other approaches that use multiple datasets simultaneously, including BERTScore(Devlin et al.,
2019; Liu & Lapata, 2020b ) ; RoBERTa+LSTM + L2SRC -
Silakan kutip/bintangi? halaman ini jika Anda menggunakan korpus ini
Jika Anda menggunakan korpus ini dalam penelitian Anda, harap gunakan entri BibTeX berikut:
@Misc{acl_anthology_corpus,
author = {Shaurya Rohatgi},
title = {ACL Anthology Corpus with Full Text},
howpublished = {Github},
year = {2022},
url = {https://github.com/shauryr/ACL-anthology-corpus}
} Kami mengucapkan terima kasih kepada Semantic Scholar yang telah memberikan akses terhadap data terkait sitasi pada korpus ini.
Korpus antologi ACL dirilis di bawah CC BY-NC 4.0. Dengan menggunakan korpus ini, Anda menyetujui ketentuan penggunaannya.
