mt5-large-finetuned-mnli-xtreme-xnli

Model Description

This model takes a pretrained large multilingual-t5 (also available from models) and fine-tunes it on English MNLI and the xtreme_xnli training set. It is intended to be used for zero-shot text classification, inspired by xlm-roberta-large-xnli.

Intended Use

This model is intended to be used for zero-shot text classification, especially in languages other than English. It is fine-tuned on English MNLI and the xtreme_xnli training set, a multilingual NLI dataset. The model can therefore be used with any of the languages in the XNLI corpus:

  • Arabic
  • Bulgarian
  • Chinese
  • English
  • French
  • German
  • Greek
  • Hindi
  • Russian
  • Spanish
  • Swahili
  • Thai
  • Turkish
  • Urdu
  • Vietnamese

As per recommendations in xlm-roberta-large-xnli, for English-only classification, you might want to check out:

Zero-shot example:

The model retains its text-to-text characteristic after fine-tuning. This means that our expected outputs will be text. During fine-tuning, the model learns to respond to the NLI task with a series of single token responses that map to entailment, neutral, or contradiction. The NLI task is indicated with a fixed prefix, "xnli:".

Below is an example, using PyTorch, of the model's use in a similar fashion to the zero-shot-classification pipeline. We use the logits from the LM output at the first token to represent confidence.

from torch.nn.functional import softmax
from transformers import MT5ForConditionalGeneration, MT5Tokenizer

model_name = "alan-turing-institute/mt5-large-finetuned-mnli-xtreme-xnli"

tokenizer = MT5Tokenizer.from_pretrained(model_name)
model = MT5ForConditionalGeneration.from_pretrained(model_name)
model.eval()

sequence_to_classify = "¿A quién vas a votar en 2020?"
candidate_labels = ["Europa", "salud pública", "política"]
hypothesis_template = "Este ejemplo es {}."

ENTAILS_LABEL = "▁0"
NEUTRAL_LABEL = "▁1"
CONTRADICTS_LABEL = "▁2"

label_inds = tokenizer.convert_tokens_to_ids(
    [ENTAILS_LABEL, NEUTRAL_LABEL, CONTRADICTS_LABEL])


def process_nli(premise: str, hypothesis: str):
    """ process to required xnli format with task prefix """
    return "".join(['xnli: premise: ', premise, ' hypothesis: ', hypothesis])


# construct sequence of premise, hypothesis pairs
pairs = [(sequence_to_classify, hypothesis_template.format(label)) for label in
        candidate_labels]
# format for mt5 xnli task
seqs = [process_nli(premise=premise, hypothesis=hypothesis) for
        premise, hypothesis in pairs]
print(seqs)
# ['xnli: premise: ¿A quién vas a votar en 2020? hypothesis: Este ejemplo es Europa.',
# 'xnli: premise: ¿A quién vas a votar en 2020? hypothesis: Este ejemplo es salud pública.',
# 'xnli: premise: ¿A quién vas a votar en 2020? hypothesis: Este ejemplo es política.']

inputs = tokenizer.batch_encode_plus(seqs, return_tensors="pt", padding=True)

out = model.generate(**inputs, output_scores=True, return_dict_in_generate=True,
                     num_beams=1)

# sanity check that our sequences are expected length (1 + start token + end token = 3)
for i, seq in enumerate(out.sequences):
    assert len(
        seq) == 3, f"generated sequence {i} not of expected length, 3." \\\\
                   f" Actual length: {len(seq)}"

# get the scores for our only token of interest
# we'll now treat these like the output logits of a `*ForSequenceClassification` model
scores = out.scores[0]

# scores has a size of the model's vocab.
# However, for this task we have a fixed set of labels
# sanity check that these labels are always the top 3 scoring
for i, sequence_scores in enumerate(scores):
    top_scores = sequence_scores.argsort()[-3:]
    assert set(top_scores.tolist()) == set(label_inds), \\\\
        f"top scoring tokens are not expected for this task." \\\\
        f" Expected: {label_inds}. Got: {top_scores.tolist()}."

# cut down scores to our task labels
scores = scores[:, label_inds]
print(scores)
# tensor([[-2.5697,  1.0618,  0.2088],
#         [-5.4492, -2.1805, -0.1473],
#         [ 2.2973,  3.7595, -0.1769]])


# new indices of entailment and contradiction in scores
entailment_ind = 0
contradiction_ind = 2

# we can show, per item, the entailment vs contradiction probas
entail_vs_contra_scores = scores[:, [entailment_ind, contradiction_ind]]
entail_vs_contra_probas = softmax(entail_vs_contra_scores, dim=1)
print(entail_vs_contra_probas)
# tensor([[0.0585, 0.9415],
#         [0.0050, 0.9950],
#         [0.9223, 0.0777]])


# or we can show probas similar to `ZeroShotClassificationPipeline`
# this gives a zero-shot classification style output across labels
entail_scores = scores[:, entailment_ind]
entail_probas = softmax(entail_scores, dim=0)
print(entail_probas)
# tensor([7.6341e-03, 4.2873e-04, 9.9194e-01])

print(dict(zip(candidate_labels, entail_probas.tolist())))
# {'Europa': 0.007634134963154793,
# 'salud pública': 0.0004287279152777046,
# 'política': 0.9919371604919434}

Unfortunately, the generate function for the TF equivalent model doesn't exactly mirror the PyTorch version so the above code won't directly transfer.

The model is currently not compatible with the existing zero-shot-classification pipeline.

Training

This model was pre-trained on a set of 101 languages in the mC4, as described in the mt5 paper. It was then fine-tuned on the mt5_xnli_translate_train task for 8k steps in a similar manner to that described in the offical repo, with guidance from Stephen Mayhew's notebook. The resulting model was then converted to :hugging_face: format.

Eval results

Accuracy over XNLI test set:

ar bg de el en es fr hi ru sw th tr ur vi zh average
81.0 85.0 84.3 84.3 88.8 85.3 83.9 79.9 82.6 78.0 81.0 81.6 76.4 81.7 82.3 82.4
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