## Pretrained Models |**Sentence Length**|**Trained Tokens**|**Link**| |----------|----------|----------| |128|~11B|[BiGS-11B-128](https://drive.google.com/drive/folders/1-nhzeWVgpXwMyNEQ5j-MwJxSzwKyT2an?usp=sharing) |128|~29B|[BiGS-29B-128](https://drive.google.com/drive/folders/10Mtl8_XUJb2mmHLyRC9x1wltdIWy6aaP?usp=sharing) |128|~97B|[BiGS-97B-128](https://huggingface.co/JunxiongWang/BiGS_128) |512|~108B|[BiGS-108B-512](https://huggingface.co/JunxiongWang/BiGS_512) |1024|~110B|[BiGS-110B-1024](https://huggingface.co/JunxiongWang/BiGS_1024) |4096|~110B|[BiGS-110B-4096](https://huggingface.co/JunxiongWang/BiGS_4096) ### MNLI Checkpoints |**Sentence Length**|**Trained Tokens**|**Model**| |----------|----------|----------| |128|~11B|[BiGS-11B-128MNLI](https://drive.google.com/drive/folders/1-tn5ar_tRi9DnK_bNMZtPpappUdNnVET?usp=sharing) |128|~29B|[BiGS-29B-128MNLI](https://drive.google.com/drive/folders/116JwMbChYp9tBuPTz5jbiaulhXrXt1P2?usp=sharing) |128|~97B|[BiGS-97B-128MNLI](https://huggingface.co/JunxiongWang/BiGS_128_MNLI) |512|~108B|[BiGS-108B-512MNLI](https://huggingface.co/JunxiongWang/BiGS_512_MNLI) ## Example Usage ### Load Masked Language Model ```python import jax from jax import numpy as jnp from transformers import BertTokenizer from BiGS.modeling_flax_bigs import FlaxBiGSForMaskedLM tokenizer = BertTokenizer.from_pretrained('bert-large-uncased') model = FlaxBiGSForMaskedLM.from_pretrained('JunxiongWang/BiGS_128') text = "The goal of life is [MASK]." encoded_input = tokenizer(text, return_tensors='np', padding='max_length', max_length=128) output = model(**encoded_input) tokenizer.convert_ids_to_tokens(jnp.flip(jnp.argsort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:10]) # output: ['happiness', 'love', 'peace', 'perfection', 'life', 'enlightenment', 'god', 'survival', 'freedom', 'good'] jnp.flip(jnp.sort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:10] # probability: [0.16052087, 0.04306792, 0.03651363, 0.03468223, 0.02927081, 0.02549769, 0.02385132, 0.02261189, 0.01672831, 0.01619471] text = "Paris is the [MASK] of France." encoded_input = tokenizer(text, return_tensors='np', padding='max_length', max_length=128) output = model(**encoded_input) tokenizer.convert_ids_to_tokens(jnp.flip(jnp.argsort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:8]) # output: ['capital', 'centre', 'center', 'city', 'capitol', 'prefecture', 'headquarters', 'president', 'metropolis', 'heart'] jnp.flip(jnp.sort(jax.nn.softmax(output.logits[encoded_input['input_ids']==103]))[0])[:10] # probability: [0.9981787 , 0.00034076, 0.00026992, 0.00026926, 0.00017787, 0.00004816, 0.00004256, 0.00003716, 0.00003634, 0.00002893] ``` ### Load Sequence Classification Model ```python from BiGS.modeling_flax_bigs import FlaxBiGSForSequenceClassification model = FlaxBiGSForSequenceClassification.from_pretrained('JunxiongWang/BiGS_512') ``` ### Load Question Answering Model ```python from BiGS.modeling_flax_bigs import FlaxBiGSForQuestionAnswering model = FlaxBiGSForQuestionAnswering.from_pretrained('JunxiongWang/BiGS_512') ``` ### Load Multiple Choice Classification Model ```python from BiGS.modeling_flax_bigs import FlaxBiGSForMultipleChoice model = FlaxBiGSForMultipleChoice.from_pretrained('JunxiongWang/BiGS_512') ```