XGLM-7.5B
XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li* (*Equal Contribution). The original implementation was released in this repository.
Training Data Statistics
The training data statistics of XGLM-7.5B is shown in the table below.
ISO-639-1 | family | name | # tokens | ratio | ratio w/ lowRes upsampling |
---|---|---|---|---|---|
en | Indo-European | English | 803526736124 | 0.489906 | 0.3259 |
ru | Indo-European | Russian | 147791898098 | 0.0901079 | 0.0602 |
zh | Sino-Tibetan | Chinese | 132770494630 | 0.0809494 | 0.0483 |
de | Indo-European | German | 89223707856 | 0.0543992 | 0.0363 |
es | Indo-European | Spanish | 87303083105 | 0.0532282 | 0.0353 |
fr | Indo-European | French | 77419639775 | 0.0472023 | 0.0313 |
ja | Japonic | Japanese | 66054364513 | 0.040273 | 0.0269 |
it | Indo-European | Italian | 41930465338 | 0.0255648 | 0.0171 |
pt | Indo-European | Portuguese | 36586032444 | 0.0223063 | 0.0297 |
el | Indo-European | Greek (modern) | 28762166159 | 0.0175361 | 0.0233 |
ko | Koreanic | Korean | 20002244535 | 0.0121953 | 0.0811 |
fi | Uralic | Finnish | 16804309722 | 0.0102455 | 0.0681 |
id | Austronesian | Indonesian | 15423541953 | 0.00940365 | 0.0125 |
tr | Turkic | Turkish | 12413166065 | 0.00756824 | 0.0101 |
ar | Afro-Asiatic | Arabic | 12248607345 | 0.00746791 | 0.0099 |
vi | Austroasiatic | Vietnamese | 11199121869 | 0.00682804 | 0.0091 |
th | TaiβKadai | Thai | 10842172807 | 0.00661041 | 0.044 |
bg | Indo-European | Bulgarian | 9703797869 | 0.00591635 | 0.0393 |
ca | Indo-European | Catalan | 7075834775 | 0.0043141 | 0.0287 |
hi | Indo-European | Hindi | 3448390110 | 0.00210246 | 0.014 |
et | Uralic | Estonian | 3286873851 | 0.00200399 | 0.0133 |
bn | Indo-European | Bengali, Bangla | 1627447450 | 0.000992245 | 0.0066 |
ta | Dravidian | Tamil | 1476973397 | 0.000900502 | 0.006 |
ur | Indo-European | Urdu | 1351891969 | 0.000824241 | 0.0055 |
sw | NigerβCongo | Swahili | 907516139 | 0.000553307 | 0.0037 |
te | Dravidian | Telugu | 689316485 | 0.000420272 | 0.0028 |
eu | Language isolate | Basque | 105304423 | 6.42035e-05 | 0.0043 |
my | Sino-Tibetan | Burmese | 101358331 | 6.17976e-05 | 0.003 |
ht | Creole | Haitian, Haitian Creole | 86584697 | 5.27902e-05 | 0.0035 |
qu | Quechuan | Quechua | 3236108 | 1.97304e-06 | 0.0001 |
Model card
For intended usage of the model, please refer to the model card released by the XGLM-7.5B development team.
Example (COPA)
The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.
import torch
import torch.nn.functional as F
from transformers import XGLMTokenizer, XGLMForCausalLM
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-7.5B")
model = XGLMForCausalLM.from_pretrained("facebook/xglm-7.5B")
data_samples = {
'en': [
{
"premise": "I wanted to conserve energy.",
"choice1": "I swept the floor in the unoccupied room.",
"choice2": "I shut off the light in the unoccupied room.",
"question": "effect",
"label": "1"
},
{
"premise": "The flame on the candle went out.",
"choice1": "I blew on the wick.",
"choice2": "I put a match to the wick.",
"question": "cause",
"label": "0"
}
],
'zh': [
{
"premise": "ζζ³θηΊ¦θ½ζΊγ",
"choice1": "ζε¨η©ΊηηζΏι΄ιζ«δΊε°ζΏγ",
"choice2": "ζζη©ΊζΏι΄ιηη―ε
³δΊγ",
"question": "effect",
"label": "1"
},
{
"premise": "θ‘ηδΈηη«η°ηηδΊγ",
"choice1": "ζεΉηδΊη―θ―γ",
"choice2": "ζζδΈζ Ήη«ζ΄ζΎε¨η―θ―δΈγ",
"question": "cause",
"label": "0"
}
],
'hi': [
{
"premise": "M te vle konsève enèji.",
"choice1": "Mwen te fin baleye chanm lib la.",
"choice2": "Mwen te femen limyè nan chanm lib la.",
"question": "effect",
"label": "1"
},
{
"premise": "Flam bouji a te etenn.",
"choice1": "Mwen te soufle bouji a.",
"choice2": "Mwen te limen mèch bouji a.",
"question": "cause",
"label": "0"
}
]
}
def get_logprobs(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:]
outputs = model(**inputs, labels=input_ids)
logits = outputs.logits
logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2))
return logprobs
# Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task.
# A return value of 0 indicates that the first alternative is more plausible,
# while 1 indicates that the second alternative is more plausible.
def COPA_eval(prompt, alternative1, alternative2):
lprob1 = get_logprobs(prompt + "\n" + alternative1).sum()
lprob2 = get_logprobs(prompt + "\n" + alternative2).sum()
return 0 if lprob1 > lprob2 else 1
for lang in data_samples_long:
for idx, example in enumerate(data_samples_long[lang]):
predict = COPA_eval(example["premise"], example["choice1"], example["choice2"])
print(f'{lang}-{idx}', predict, example['label'])
# en-0 1 1
# en-1 0 0
# zh-0 1 1
# zh-1 0 0
# hi-0 1 1
# hi-1 0 0
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