An English semantic tagging model based on bert-base-uncased
This model is a BERT-base-uncased model finetuned for semantic tagging.
As training data, I use the English fragment (both gold and silver data) from the Parallel Meaning Bank's Universal Semantic Tags dataset [1].
Inference
The model is trained to make predictions for the embedded representations corresponding to the first subword of each word. Inference in the same setting as in training can be achieved with the following code (huggingface's standard pipeline does not behave as intended here). Note that the pipeline below assumes that inputs are already split into words by spaces.
from transformers import AutoTokenizer, AutoModelForTokenClassification
from spacy_alignments.tokenizations import get_alignments
import torch
tokenizer = AutoTokenizer.from_pretrained("hfunakura/en-bertsemtagger-gold")
model = AutoModelForTokenClassification.from_pretrained("hfunakura/en-bertsemtagger-gold")
# define the tagset
id2semtag = {"0": "@@UNK@@", "1": "PRO", "2": "CTC", "3": "INT", "4": "EMP", "5": "DEC", "6": "ITJ", "7": "GRE", "8": "NEC", "9": "PFT", "10": "IMP", "11": "HAP", "12": "ROL", "13": "MOY", "14": "PRG", "15": "HAS", "16": "CLO", "17": "MOR", "18": "DEF", "19": "BUT", "20": "YOC", "21": "PRI", "22": "EQU", "23": "SUB", "24": "APX", "25": "REL", "26": "XCL", "27": "CON", "28": "GPO", "29": "QUE", "30": "DIS", "31": "IST", "32": "COL", "33": "SCO", "34": "GRP", "35": "EXS", "36": "FUT", "37": "ENS", "38": "QUC", "39": "DOM", "40": "SST", "41": "NIL", "42": "COO", "43": "QUV", "44": "PST", "45": "UNK", "46": "EXT", "47": "NTH", "48": "LIT", "49": "ORG", "50": "EXG", "51": "REF", "52": "DOW", "53": "TOP", "54": "EPS", "55": "DXT", "56": "AND", "57": "UOM", "58": "ALT", "59": "POS", "60": "PRX", "61": "GEO", "62": "BOT", "63": "DEG", "64": "ART", "65": "PER", "66": "GPE", "67": "EFS", "68": "DST", "69": "LES", "70": "ORD", "71": "NOT", "72": "NOW", "-100": "@@PAD@@"}
class SemtaggerPipeline():
def __init__(self, model, tokenizer, id2semtag):
self.model = model
self.tokenizer = tokenizer
self.id2semtag = id2semtag
def predict(self, text):
# get alignments
encoding_list = self.tokenizer(text, add_special_tokens=False)
encoded_tokens = self.tokenizer.convert_ids_to_tokens(encoding_list["input_ids"])
words = text.split(" ")
alignments = get_alignments(encoded_tokens, words)[1]
is_first_list = []
for alignment in alignments:
is_first_list += [1] + [0]*(len(alignment)-1)
is_first = torch.tensor(is_first_list)
# yield and extract predictions
encoding = self.tokenizer(text, return_tensors="pt", add_special_tokens=False)
logits = model(**encoding).logits
preds = logits.argmax(-1)[0][is_first==1]
pred_labels = [self.id2semtag[str(int(i))] for i in preds]
result = [f"{word}/{label}" for word, label in zip(words,pred_labels)]
return " ".join(result)
pipeline = SemtaggerPipeline(model, tokenizer, id2semtag)
pipeline.predict("Jim and Mary smiled and left .")
References
[1] Lasha Abzianidze, Johan Bos (2017): Towards Universal Semantic Tagging. Proceedings of the 12th International Conference on Computational Semantics (IWCS 2017) -- Short Papers, pp 1–6, Montpellier, France, https://pmb.let.rug.nl/data.php.
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