diff --git a/code/contextual_embeddings/configs/bert.jsonnet b/code/contextual_embeddings/configs/bert.jsonnet
index 4df2d3ef50289765f1074525602074943e7c5769..4a0b9853d49ec75d85eaa9bee5dedb4e0195bff6 100644
--- a/code/contextual_embeddings/configs/bert.jsonnet
+++ b/code/contextual_embeddings/configs/bert.jsonnet
@@ -67,6 +67,6 @@
"num_serialized_models_to_keep": 3,
"num_epochs": 10,
"grad_norm": 5.0,
- "cuda_device": 0
+ "cuda_device": -1
}
}
diff --git a/code/ssplit/README.md b/code/ssplit/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..eae79e543ca20308c771bd9b6a057c0ecc706360
--- /dev/null
+++ b/code/ssplit/README.md
@@ -0,0 +1,23 @@
+#Requirements
+
+`ersatz` (`pip install ersatz`)
+
+#Usage
+
+`python tok2conllu.py <file>`
+
+File must be a `.tok` file.
+
+#Output
+
+`<file>_conllu`: same file but sentences are separated by a line-jump and a commented line with sentence id.
+
+#Ersatz library
+
+Command-line usage:
+
+`ersatz --input <input.txt> --output <output.txt>`
+
+Takes as input any text file and outputs the same text file with sentences separated by a line-break.
+
+
diff --git a/code/ssplit/parse_stanza.py b/code/ssplit/parse_stanza.py
index b5367fb9b2e1592df161b933e1a4fd60b9024590..c4373a75268e18a811c7992ec641f2c44c4b389e 100644
--- a/code/ssplit/parse_stanza.py
+++ b/code/ssplit/parse_stanza.py
@@ -65,4 +65,4 @@ def ssplit_stanza(lang, fp_toks, out_dir, treebank=None):
f_out.write('\t'.join(row)+'\n')
#print('\t'.join(row).encode('utf-8'), file=f_out)
f_out.write('\n')
- #print('', file=f_out)
\ No newline at end of file
+ #print('', file=f_out)
diff --git a/code/ssplit/tok2conllu.py b/code/ssplit/tok2conllu.py
new file mode 100644
index 0000000000000000000000000000000000000000..a223a0e0dfe8ac02ddb984296edeac4fd9e3d34f
--- /dev/null
+++ b/code/ssplit/tok2conllu.py
@@ -0,0 +1,87 @@
+import sys
+import pandas as pd
+import os
+
+tab = "\t"
+space = " "
+
+def parse_file(f):
+ """Take a .tok file and turn it into a sequence of token ids and tokens (.tok_seq). Token id precedes token."""
+
+ column_names = ['tok_id', 'tok', '1', '2', '3', '4', '5', '6', '7', 'seg']
+
+ dataframe = pd.read_csv(f, names=column_names, comment="#", sep="\t",skipinitialspace=True)
+ tok_ids = dataframe['tok_id'].values
+ toks = dataframe['tok'].values
+
+ return dataframe, tok_ids, toks
+
+def write_seq_file(f, tok_ids, toks):
+ """Write sequence of token ids and tokens to a .tok_ssplit file"""
+
+ with open(f, "w") as wf:
+ for i, tok in zip(tok_ids, toks):
+ wf.write(str(i) + tab + tok + tab)
+
+def parse_ssplit_file(f):
+ """Take a .tok_ssplit file and return ids of sentence-beginning tokens."""
+
+ with open(f, "r") as rf:
+ sentences = rf.readlines()
+
+ sstart_ids = [0] * len(sentences)
+ for i, sentence in enumerate(sentences):
+ ids_toks = sentence.strip().split(space)
+ sstart_ids[i] = ids_toks[0]
+
+ return sstart_ids
+
+def make_ssplit(rf, wf, sstart_ids):
+ """Write new file with sentence boundaries"""
+ with open(rf, "r") as f:
+ lines = f.readlines()
+
+ doc_id = None
+ next_sentence = 0 #index of token beginning next sentence in sstart_ids
+ sent_counter = 0
+
+ with open(wf, "w") as f:
+ for line in lines:
+ split = line.strip().split(tab)
+ tok_id = split[0]
+ if tok_id.startswith("#"):
+ doc_id = line
+ sent_counter = 0
+ f.write(line)
+ elif tok_id == sstart_ids[next_sentence]:
+ doc_id_nb = doc_id.strip().split("= ")[1]
+ if sent_counter: newline = "\n"
+ else: newline = ""
+ sent_counter += 1
+ sent_id = "# sent_id = " + doc_id_nb + "-" + str(sent_counter)
+ f.write(newline + sent_id + "\n")
+ f.write(line)
+ next_sentence += 1
+ else:
+ f.write(line)
+
+def t2c(f):
+ dataframe, tok_ids, toks = parse_file(f)
+ f_seq = f + "_seq"
+ write_seq_file(f_seq, tok_ids, toks)
+ f_ssplit = f + "_ssplit"
+ os.system(f"ersatz --input {f_seq} --output {f_ssplit}")
+ sstart_ids = parse_ssplit_file(f_ssplit)
+ f_conllu = f + "_conllu"
+ make_ssplit(f, f_conllu, sstart_ids)
+ os.system(f"rm {f_seq} {f_ssplit}") #remove temporary files
+
+def main():
+ if len(sys.argv) < 2:
+ print("usage: python tok2conllu.py <file>")
+ sys.exit()
+ for f in sys.argv[1:]:
+ t2c(f)
+
+if __name__ == '__main__':
+ main()
diff --git a/code/utils/get_scores.py b/code/utils/get_scores.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf505bc239b4ce1af678313d7dc4f2ebeff9f105
--- /dev/null
+++ b/code/utils/get_scores.py
@@ -0,0 +1,54 @@
+import sys
+import os
+import pandas as pd
+
+#Must be executed from discut dir
+
+def parse_score(line):
+ split = line.strip().split(": ")
+ return str((round(float(split[-1]), 4) * 100))[:5]
+
+def parse_model(line):
+ split = line.strip().split("_")
+ model_ft = split[1].strip().split("-")
+ if len(model_ft) > 1: model = model_ft
+ else: model = [model_ft[0], "n/a"]
+ return model
+
+def parse_test(line):
+ split = line.strip().split("_")
+ return split[0]
+
+def write_scores(result_dir, output_file):
+ if not os.path.isdir(result_dir):
+ print(f"directory {result_dir} doesn't exist.")
+
+ _, dirs, _ = next(iter(os.walk(result_dir)))
+ table = []
+
+ for d in dirs:
+ files = os.listdir(os.path.join(result_dir, d))
+ for fil in files:
+ if fil.endswith('test.scores'):
+ with open(os.path.join(result_dir, d, fil), "r") as r_f:
+ lines = r_f.readlines()
+ precision, recall, fscore = [parse_score(l) for l in lines[-3:]]
+ model = parse_model(d)
+ entry = [model[0], model[1], parse_test(fil), precision, recall, fscore]
+ table.append(entry)
+
+ columns = ['Model', 'Fine-tuning', 'Test', 'Precision', 'Recall', 'F1']
+ df = pd.DataFrame(table, columns = columns)
+ df.to_csv((output_file + ".tsv"), sep="\t", index=False)
+
+def main():
+ if len(sys.argv) != 3:
+ print("usage: python get_scores.py <result_dir> <output_file>"); sys.exit()
+ else:
+ result_dir = sys.argv[1]
+ output_file = sys.argv[2]
+ write_scores(result_dir, output_file)
+
+if __name__ == '__main__':
+ main()
+
diff --git a/scores_expes/220622_conllu.tsv b/scores_expes/220622_conllu.tsv
new file mode 100644
index 0000000000000000000000000000000000000000..8414370029fca110f52a378265dd779bfd51f6e7
--- /dev/null
+++ b/scores_expes/220622_conllu.tsv
@@ -0,0 +1,39 @@
+Model Fine-tuning Test Precision Recall F1
+fra.sdrt.annodis n/a fra.sdrt.annodis 86.85 88.67 87.75
+fra.sdrt.annodis n/a deu.rst.pcc 72.96 97.28 83.38
+fra.sdrt.annodis n/a eng.sdrt.stac 94.42 89.72 92.01
+fra.sdrt.annodis n/a eus.rst.ert 56.98 87.7 69.08
+fra.sdrt.annodis n/a fas.rst.prstc 79.5 86.27 82.75
+deu.rst.pcc n/a deu.rst.pcc 95.62 96.6 96.11
+deu.rst.pcc n/a rus.rst.rrt 69.38 77.88 73.38
+deu.rst.pcc n/a zho.rst.sctb 54.76 95.83 69.69
+deu.rst.pcc n/a eng.sdrt.stac 98.34 88.17 92.97
+deu.rst.pcc n/a eus.rst.ert 77.85 79.32 78.58
+deu.rst.pcc n/a fas.rst.prstc 91.57 68.06 78.08
+deu.rst.pcc n/a fra.sdrt.annodis 86.00 66.67 75.11
+deu.rst.pcc n/a nld.rst.nldt 92.55 88.17 90.3
+deu.rst.pcc n/a spa.rst.rststb 71.31 91.3 80.08
+deu.rst.pcc n/a por.rst.cstn 82.44 70.59 76.06
+eus.rst.ert n/a deu.rst.pcc 87.06 91.5 89.22
+eus.rst.ert n/a eng.sdrt.stac 99.05 87.2 92.75
+eus.rst.ert n/a eus.rst.ert 90.06 83.24 86.52
+eus.rst.ert n/a fas.rst.prstc 85.34 68.66 76.1
+fas.rst.prstc n/a deu.rst.pcc 81.55 93.2 86.98
+fas.rst.prstc n/a eng.sdrt.stac 98.76 87.46 92.77
+fas.rst.prstc n/a eus.rst.ert 74.49 79.32 76.83
+fas.rst.prstc n/a fas.rst.prstc 92.38 92.24 92.31
+deu.rst.pcc deu.rst.pcc deu.rst.pcc 95.88 94.89 95.38
+deu.rst.pcc eus.rst.ert deu.rst.pcc 98.58 94.56 96.53
+deu.rst.pcc fas.rst.prstc deu.rst.pcc 94.65 96.26 95.45
+deu.rst.pcc fra.sdrt.annodis deu.rst.pcc 96.86 94.56 95.7
+eng.sdrt.stac deu.rst.pcc eng.sdrt.stac 94.69 95.67 95.17
+eng.sdrt.stac eus.rst.ert eng.sdrt.stac 96.46 93.47 94.94
+eng.sdrt.stac fas.rst.prstc eng.sdrt.stac 96.41 94.05 95.22
+eng.sdrt.stac fra.sdrt.annodis eng.sdrt.stac 96.82 94.51 95.65
+eus.rst.ert deu.rst.pcc eus.rst.ert 90.62 82.3 86.26
+eus.rst.ert eus.rst.ert eus.rst.ert 87.31 86.49 86.9
+eus.rst.ert fas.rst.prstc eus.rst.ert 89.55 81.08 85.11
+eus.rst.ert fra.sdrt.annodis eus.rst.ert 92.53 82.03 86.96
+fas.rst.prstc deu.rst.pcc fas.rst.prstc 88.94 93.73 91.28
+fas.rst.prstc eus.rst.ert fas.rst.prstc 93.05 91.94 92.49
+fas.rst.prstc fas.rst.prstc fas.rst.prstc 93.8 90.3 92.02