Model Card for udever-bloom

udever-bloom-3b is finetuned from bigscience/bloom-3b via BitFit on MS MARCO Passage Ranking, SNLI and MultiNLI data. It is a universal embedding model across tasks, natural and programming languages. (From the technical view, udever is merely with some minor improvements to sgpt-bloom)

Model Details

Model Description

Developed by: Alibaba Group
Model type: Transformer-based Language Model (decoder-only)
Language(s) (NLP): Multiple; see bloom training data
Finetuned from model : bigscience/bloom-3b

Model Sources

Repository: github.com/izhx/uni-rep
Paper : Language Models are Universal Embedders
Training Date : 2023-06

How to Get Started with the Model

Use the code below to get started with the model.

import torch
from transformers import AutoTokenizer, BloomModel

tokenizer = AutoTokenizer.from_pretrained('izhx/udever-bloom-3b')
model = BloomModel.from_pretrained('izhx/udever-bloom-3b')

boq, eoq, bod, eod = '[BOQ]', '[EOQ]', '[BOD]', '[EOD]'
eoq_id, eod_id = tokenizer.convert_tokens_to_ids([eoq, eod])

if tokenizer.padding_side != 'left':
    print('!!!', tokenizer.padding_side)
    tokenizer.padding_side = 'left'


def encode(texts: list, is_query: bool = True, max_length=300):
    bos = boq if is_query else bod
    eos_id = eoq_id if is_query else eod_id
    texts = [bos + t for t in texts]
    encoding = tokenizer(
        texts, truncation=True, max_length=max_length - 1, padding=True
    )
    for ids, mask in zip(encoding['input_ids'], encoding['attention_mask']):
        ids.append(eos_id)
        mask.append(1)
    inputs = tokenizer.pad(encoding, return_tensors='pt')
    with torch.inference_mode():
        outputs = model(**inputs)
        embeds = outputs.last_hidden_state[:, -1]
    return embeds

encode(['I am Bert', 'You are Elmo'])

Training Details

Training Data

MS MARCO Passage Ranking, retrieved by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86)
SNLI and MultiNLI (https://sbert.net/datasets/AllNLI.tsv.gz)

Training Procedure

Preprocessing

MS MARCO hard negatives provided by (https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/ms_marco/train_bi-encoder_mnrl.py#L86). Negatives for SNLI and MultiNLI are randomly sampled.

Training Hyperparameters

Training regime: tf32, BitFit
Batch size: 1024
Epochs: 3
Optimizer: AdamW
Learning rate: 1e-4
Scheduler: constant with warmup.
Warmup: 0.25 epoch

Evaluation

Table 1: Massive Text Embedding Benchmark MTEB

MTEB	Avg.	Class.	Clust.	PairClass.	Rerank.	Retr.	STS	Summ.
#Datasets ➡️	56	12	11	3	4	15	10	1

bge-large-en-v1.5	64.23	75.97	46.08	87.12	60.03	54.29	83.11	31.61
bge-base-en-v1.5	63.55	75.53	45.77	86.55	58.86	53.25	82.4	31.07
gte-large	63.13	73.33	46.84	85	59.13	52.22	83.35	31.66
gte-base	62.39	73.01	46.2	84.57	58.61	51.14	82.3	31.17
e5-large-v2	62.25	75.24	44.49	86.03	56.61	50.56	82.05	30.19
instructor-xl	61.79	73.12	44.74	86.62	57.29	49.26	83.06	32.32
instructor-large	61.59	73.86	45.29	85.89	57.54	47.57	83.15	31.84
e5-base-v2	61.5	73.84	43.8	85.73	55.91	50.29	81.05	30.28
e5-large	61.42	73.14	43.33	85.94	56.53	49.99	82.06	30.97
text-embedding-ada-002 (OpenAI API)	60.99	70.93	45.9	84.89	56.32	49.25	80.97	30.8
e5-base	60.44	72.63	42.11	85.09	55.7	48.75	80.96	31.01
SGPT-5.8B-msmarco	58.93	68.13	40.34	82	56.56	50.25	78.1	31.46
sgpt-bloom-7b1-msmarco	57.59	66.19	38.93	81.9	55.65	48.22	77.74	33.6

Udever-bloom-560m	55.80	68.04	36.89	81.05	52.60	41.19	79.93	32.06
Udever-bloom-1b1	58.28	70.18	39.11	83.11	54.28	45.27	81.52	31.10
Udever-bloom-3b	59.86	71.91	40.74	84.06	54.90	47.67	82.37	30.62
Udever-bloom-7b1	60.63	72.13	40.81	85.40	55.91	49.34	83.01	30.97

Table 2: CodeSearchNet

CodeSearchNet	Go	Ruby	Python	Java	JS	PHP	Avg.
CodeBERT	69.3	70.6	84.0	86.8	74.8	70.6	76.0
GraphCodeBERT	84.1	73.2	87.9	75.7	71.1	72.5	77.4
cpt-code S	97.7	86.3	99.8	94.0	86.0	96.7	93.4
cpt-code M	97.5	85.5	99.9	94.4	86.5	97.2	93.5
sgpt-bloom-7b1-msmarco	76.79	69.25	95.68	77.93	70.35	73.45	77.24

Udever-bloom-560m	75.38	66.67	96.23	78.99	69.39	73.69	76.73
Udever-bloom-1b1	78.76	72.85	97.67	82.77	74.38	78.97	80.90
Udever-bloom-3b	80.63	75.40	98.02	83.88	76.18	79.67	82.29
Udever-bloom-7b1	79.37	76.59	98.38	84.68	77.49	80.03	82.76

Table 3: Chinese multi-domain retrieval Multi-cpr

			E-commerce		Entertainment video		Medical
Model	Train	Backbone	MRR@10	Recall@1k	MRR@10	Recall@1k	MRR@10	Recall@1k

BM25	-	-	0.225	0.815	0.225	0.780	0.187	0.482
Doc2Query	-	-	0.239	0.826	0.238	0.794	0.210	0.505
DPR-1	In-Domain	BERT	0.270	0.921	0.254	0.934	0.327	0.747
DPR-2	In-Domain	BERT-CT	0.289	0.926	0.263	0.935	0.339	0.769
text-embedding-ada-002	General	GPT	0.183	0.825	0.159	0.786	0.245	0.593
sgpt-bloom-7b1-msmarco	General	BLOOM	0.242	0.840	0.227	0.829	0.311	0.675

Udever-bloom-560m	General	BLOOM	0.156	0.802	0.149	0.749	0.245	0.571
Udever-bloom-1b1	General	BLOOM	0.244	0.863	0.208	0.815	0.241	0.557
Udever-bloom-3b	General	BLOOM	0.267	0.871	0.228	0.836	0.288	0.619
Udever-bloom-7b1	General	BLOOM	0.296	0.889	0.267	0.907	0.343	0.705

More results refer to paper section 3.

Technical Specifications

Model Architecture and Objective

Model: bigscience/bloom-3b.
Objective: Constrastive loss with hard negatives (refer to paper section 2.2).

Compute Infrastructure

Nvidia A100 SXM4 80GB.
torch 2.0.0, transformers 4.29.2.

Citation

BibTeX:

@article{zhang2023language,
  title={Language Models are Universal Embedders},
  author={Zhang, Xin and Li, Zehan and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Zhang, Min},
  journal={arXiv preprint arXiv:2310.08232},
  year={2023}
}

Frazic
/

udever-bloom-3b-sentence