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text-classification | transformers | # DeBERTa-v3-small-mnli-fever-docnli-ling-2c
## Model description
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "I first thought that I liked the movie, but upon second thought the movie was actually disappointing. [SEP] The movie was good."}]} | MoritzLaurer/DeBERTa-v3-small-mnli-fever-docnli-ling-2c | null | [
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| DeBERTa-v3-small-mnli-fever-docnli-ling-2c
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Model description
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This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: MultiNLI, Fever-NLI, LingNLI and DocNLI (which includes ANLI, QNLI, DUC, CNN/DailyMail, Curation).
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zero-shot-classification | transformers | # DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary
## Model description
This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](http... | {"language": ["en"], "license": "mit", "tags": ["text-classification", "zero-shot-classification"], "datasets": ["multi_nli", "anli", "fever", "lingnli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary | null | [
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| DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary
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text-classification | transformers | # MiniLM-L6-mnli-binary
## Model description
This model was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli) dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailm... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "I liked the movie. [SEP] The movie was good."}]} | MoritzLaurer/MiniLM-L6-mnli-binary | null | [
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| # MiniLM-L6-mnli-binary
## Model description
This model was trained on the MultiNLI dataset. The model was trained for binary NLI, which means that the "neutral" and "contradiction" classes were merged into one class. The model therefore predicts "entailment" or "not_entailment".
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text-classification | transformers | # MiniLM-L6-mnli-fever-docnli-ling-2c
## Model description
This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxi... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "I first thought that I liked the movie, but upon second thought the movie was actually disappointing. [SEP] The movie was good."}]} | MoritzLaurer/MiniLM-L6-mnli-fever-docnli-ling-2c | null | [
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Model description
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text-classification | transformers | # MiniLM-L6-mnli
## Model description
This model was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli) dataset.
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.
## Intended uses & limitations
#### How to use the model
```python
from transf... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "metrics": ["accuracy"], "widget": [{"text": "I liked the movie. [SEP] The movie was good."}]} | MoritzLaurer/MiniLM-L6-mnli | null | [
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| # MiniLM-L6-mnli
## Model description
This model was trained on the MultiNLI dataset.
The base model is MiniLM-L6 from Microsoft, which is very fast, but a bit less accurate than other models.
## Intended uses & limitations
#### How to use the model
### Training data
MultiNLI.
### Training procedure
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text-classification | transformers |
# Covid-Policy-RoBERTa-21
This model is currently in development at the Centre for European Policy Studies (CEPS).
The model is not yet recommended for use. A more detailed description will follow.
If you are interested in using deep learning to identify 20 different types policy measures against COVID-19 in text (N... | {"language": ["en"], "tags": ["text-classification"], "metrics": ["accuracy (balanced)", "F1 (weighted)"], "widget": [{"text": "All non-essential work activity will stop in Spain from tomorrow until 9 April but there is some confusion as to which jobs can continue under the new lockdown restrictions"}]} | MoritzLaurer/covid-policy-roberta-21 | null | [
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# Covid-Policy-RoBERTa-21
This model is currently in development at the Centre for European Policy Studies (CEPS).
The model is not yet recommended for use. A more detailed description will follow.
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zero-shot-classification | transformers | # Multilingual mDeBERTa-v3-base-mnli-xnli
## Model description
This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual
zero-shot classification. The underlying model was pre-trained by Microsoft on the
[CC100 multilingual dataset](https://h... | {"language": ["multilingual", "en", "ar", "bg", "de", "el", "es", "fr", "hi", "ru", "sw", "th", "tr", "ur", "vi", "zh"], "license": "mit", "tags": ["zero-shot-classification", "text-classification", "nli", "pytorch"], "datasets": ["multi_nli", "xnli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"... | MoritzLaurer/mDeBERTa-v3-base-mnli-xnli | null | [
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Model description
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text-classification | transformers |
# Policy-DistilBERT-7d
## Model description
This model was trained on 129.669 manually annotated sentences to classify text into one of seven political categories: 'Economy', 'External Relations', 'Fabric of Society', 'Freedom and Democracy', 'Political System', 'Welfare and Quality of Life' or 'Social Groups'.
##... | {"language": ["en"], "tags": ["text-classification"], "metrics": ["accuracy (balanced)", "F1 (weighted)"], "widget": [{"text": "70-85% of the population needs to get vaccinated against the novel coronavirus to achieve herd immunity."}]} | MoritzLaurer/policy-distilbert-7d | null | [
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| Policy-DistilBERT-7d
====================
Model description
-----------------
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zero-shot-classification | transformers | # xtremedistil-l6-h256-mnli-fever-anli-ling-binary
## Model description
This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](h... | {"language": ["en"], "tags": ["text-classification", "zero-shot-classification"], "datasets": ["multi_nli", "anli", "fever", "lingnli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | MoritzLaurer/xtremedistil-l6-h256-mnli-fever-anli-ling-binary | null | [
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fill-mask | transformers |
# TswanaBert
Pretrained model on the Tswana language using a masked language modeling (MLM) objective.
## Model Description.
TswanaBERT is a transformer model pre-trained on a corpus of Setswana in a self-supervised fashion by masking part of the input words and training to predict the masks by using byte-level token... | {"language": "tn"} | MoseliMotsoehli/TswanaBert | null | [
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|
# TswanaBert
Pretrained model on the Tswana language using a masked language modeling (MLM) objective.
## Model Description.
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fill-mask | transformers |
# zuBERTa
zuBERTa is a RoBERTa style transformer language model trained on zulu text.
## Intended uses & limitations
The model can be used for getting embeddings to use on a down-stream task such as question answering.
#### How to use
```python
>>> from transformers import pipeline
>>> from transformers import Auto... | {"language": "zu"} | MoseliMotsoehli/zuBERTa | null | [
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# zuBERTa
zuBERTa is a RoBERTa style transformer language model trained on zulu text.
## Intended uses & limitations
The model can be used for getting embeddings to use on a down-stream task such as question answering.
#### How to use
## Training data
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-sst2-mahtab
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "distilbert-sst2-mahtab", "results": []}]} | Motahar/distilbert-sst2-mahtab | null | [
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|
# distilbert-sst2-mahtab
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4982
- eval_accuracy: 0.8830
- eval_runtime: 2.3447
- eval_samples_per_second: 371.91
- eval_steps_per_second: 4... | [
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text2text-generation | transformers | ### Description:
BART Model has been finetuned on CNN/DailyMail Dataset with Sample Size 10000.
### How To Use:
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
src_text = [" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is ... | {} | Mousumi/finetuned_bart | null | [
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#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| ### Description:
BART Model has been finetuned on CNN/DailyMail Dataset with Sample Size 10000.
### How To Use:
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text2text-generation | transformers | ### Description:
Pegasus Model has been finetuned on CNN/DailyMail Dataset with Sample Size 10000.
### How To Use:
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
src_text = [" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim ... | {} | Mousumi/finetuned_pegasus | null | [
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#transformers #pytorch #pegasus #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ### Description:
Pegasus Model has been finetuned on CNN/DailyMail Dataset with Sample Size 10000.
### How To Use:
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] |
text-generation | transformers | kakao brain에서 공개한 kogpt 6b model('kakaobrain/kogpt')을 fp16으로 저장한 모델입니다.
### 카카오브레인 모델을 fp16으로 로드하는 방법
```python
import torch
from transformers import GPTJForCausalLM
model = GPTJForCausalLM.from_pretrained('kakaobrain/kogpt', cache_dir='./my_dir', revision='KoGPT6B-ryan1.5b', torch_dtype=torch.float16)
```
### fp16... | {} | MrBananaHuman/kogpt_6b_fp16 | null | [
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"gptj",
"text-generation",
"autotrain_compatible",
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"region:us"
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#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us
| kakao brain에서 공개한 kogpt 6b model('kakaobrain/kogpt')을 fp16으로 저장한 모델입니다.
### 카카오브레인 모델을 fp16으로 로드하는 방법
### fp16 모델 로드 후 문장 생성
![Open In Colab](URL
### 참고 링크
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] |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | {"title": "DPT Large", "emoji": "\ud83d\udc20", "colorFrom": "red", "colorTo": "blue", "sdk": "gradio", "app_file": "app.py", "pinned": false} | MrBodean/Depthmap | null | [
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#region-us
|
# Configuration
'title': _string_
Display title for the Space
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Space emoji (emoji-only character allowed)
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Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
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text-generation | transformers |
#Rick DialoGPT model | {"tags": ["conversational"]} | MrDuckerino/DialoGPT-medium-Rick | null | [
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text-generation | transformers |
#Sarge | {"tags": ["conversational"]} | MrE/DialoGPT-medium-SARGE | null | [
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text-generation | transformers | # Sarge | {"tags": ["conversational"]} | MrE/DialoGPT-medium-SARGER1 | null | [
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text-generation | transformers | #Sarge3 | {"tags": ["conversational"]} | MrE/DialoGPT-medium-SARGER3 | null | [
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"text-generation",
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text-generation | transformers |
#Delta Chat Model | {"pipeline_tag": "conversational"} | MrGentle/DeltaModel-genius1 | null | [
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"endpoints_compatible",
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"region:us"
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text-generation | transformers | #Rick Sanchez DialoGPT model | {"tags": ["conversational"]} | MrZ/DialoGPT-small-Rick | null | [
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"text-generation-inference",
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| #Rick Sanchez DialoGPT model | [] | [
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sentence-similarity | sentence-transformers |
# SBERT-base-msmarco-asym
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SBERT-base-msmarco-asym | null | [
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"arxiv:2202.08904",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2202.08904"
] | [] | TAGS
#sentence-transformers #feature-extraction #sentence-similarity #arxiv-2202.08904 #endpoints_compatible #region-us
|
# SBERT-base-msmarco-asym
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers |
# SBERT-base-msmarco-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloade... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SBERT-base-msmarco-bitfit | null | [
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"feature-extraction",
"sentence-similarity",
"transformers",
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"endpoints_compatible",
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"2202.08904"
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#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #arxiv-2202.08904 #endpoints_compatible #region-us
|
# SBERT-base-msmarco-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers |
# SBERT-base-msmarco
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataL... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SBERT-base-msmarco | null | [
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|
# SBERT-base-msmarco
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers | This model is used in "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning".
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sentence-similarity | sentence-transformers |
# SBERT-base-nli-v2-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datas... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SBERT-base-nli-v2-bitfit | null | [
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# SBERT-base-nli-v2-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SBERT-base-nli-v2
This model is used in "SGPT: GPT Sentence Embeddings for Semantic Search" and "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning".
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluati... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SBERT-base-nli-v2 | null | [
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# SBERT-base-nli-v2
This model is used in "SGPT: GPT Sentence Embeddings for Semantic Search" and "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning".
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, re... | [
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sentence-similarity | sentence-transformers |
# SBERT-large-nli-v2
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.N... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SBERT-large-nli-v2 | null | [
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# SBERT-large-nli-v2
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-1.3B-mean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.No... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-1.3B-mean-nli | null | [
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"region:us"
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|
# SGPT-1.3B-mean-nli
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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feature-extraction | sentence-transformers |
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataL... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"], "model-index": [{"name": "SGPT-1.3B-weightedmean-msmarco-specb-bitfit", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "con... | Muennighoff/SGPT-1.3B-weightedmean-msmarco-specb-bitfit | null | [
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|
# SGPT-1.3B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to the eval folder or our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-1.3B-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-1.3B-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to the eval folder or our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-1.3B-weightedmean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.dat... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-1.3B-weightedmean-nli | null | [
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#sentence-transformers #pytorch #gpt_neo #feature-extraction #sentence-similarity #arxiv-2202.08904 #endpoints_compatible #region-us
|
# SGPT-1.3B-weightedmean-nli
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-lasttoken-msmarco-specb
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.d... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-125M-lasttoken-msmarco-specb | null | [
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|
# SGPT-125M-lasttoken-msmarco-specb
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers |
# SGPT-125M-lasttoken-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datase... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-125M-lasttoken-nli | null | [
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|
# SGPT-125M-lasttoken-nli
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 8807 ... | [
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sentence-similarity | sentence-transformers |
# SGPT-125M-learntmean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-learntmean-nli
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-mean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-mean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
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sentence-similarity | sentence-transformers |
# SGPT-125M-mean-nli-linear5
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-mean-nli-linear5
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-mean-nli-linearthenpool5
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-mean-nli-linearthenpool5
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-mean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-mean-nli
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-scratchmean-nli
** Trained from scratch only on NLI with reinitialized GPT-Neo weights **
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model wa... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-125M-scratchmean-nli | null | [
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# SGPT-125M-scratchmean-nli
Trained from scratch only on NLI with reinitialized GPT-Neo weights
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-msmarco-asym
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-weightedmean-msmarco-asym
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
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# SGPT-125M-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to the eval folder or our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-msmarco-specb-bitfitwte
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-125M-weightedmean-msmarco-specb-bitfitwte | null | [
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# SGPT-125M-weightedmean-msmarco-specb-bitfitwte
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-msmarco-specb
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.dat... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-125M-weightedmean-msmarco-specb | null | [
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|
# SGPT-125M-weightedmean-msmarco-specb
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
Loss:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-msmarco
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-weightedmean-msmarco
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 15600 with parameters:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-nli-bitfit-linearthenpool1-noact
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader*... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Muennighoff/SGPT-125M-weightedmean-nli-bitfit-linearthenpool1-noact | null | [
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# SGPT-125M-weightedmean-nli-bitfit-linearthenpool1-noact
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to the eval folder or our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-125M-weightedmean-nli
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-125M-weightedmean-nli
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-2.7B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
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# SGPT-2.7B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to the eval folder or our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-2.7B-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to the eval folder or our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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# SGPT-2.7B-weightedmean-nli-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to the eval folder or our paper: URL
## Training
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DataLoader:
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sentence-similarity | sentence-transformers |
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
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|
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 249592 with parameters:
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sentence-similarity | sentence-transformers |
# SGPT-5.8B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
## Evaluation Results
For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.ut... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "SGPT-5.8B-weightedmean-nli-bitfit", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb... | Muennighoff/SGPT-5.8B-weightedmean-nli-bitfit | null | [
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# SGPT-5.8B-weightedmean-msmarco-specb-bitfit
## Usage
For usage instructions, refer to our codebase: URL
## Evaluation Results
For eval results, refer to our paper: URL
## Training
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DataLoader:
'URL.dataloader.DataLoader' of length 249592 with parameters:
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text-classification | transformers | My First Model
- for classification of wolf | {} | Mulin/my_wolf_model | null | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 0k (uncased)
Seed 0 intermediate checkpoint 0k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-0k | null | [
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| # MultiBERTs Seed 0 Checkpoint 0k (uncased)
Seed 0 intermediate checkpoint 0k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1000k (uncased)
Seed 0 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1000k | null | [
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| # MultiBERTs Seed 0 Checkpoint 1000k (uncased)
Seed 0 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1000k (uncased)\nSeed 0 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 100k (uncased)
Seed 0 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-100k | null | [
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| # MultiBERTs Seed 0 Checkpoint 100k (uncased)
Seed 0 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 100k (uncased)\nSeed 0 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1100k (uncased)
Seed 0 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1100k | null | [
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| # MultiBERTs Seed 0 Checkpoint 1100k (uncased)
Seed 0 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1100k (uncased)\nSeed 0 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1200k (uncased)
Seed 0 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1200k | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 1200k (uncased)
Seed 0 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1200k (uncased)\nSeed 0 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 0 Checkpoint 120k (uncased)
Seed 0 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-120k | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 120k (uncased)
Seed 0 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 120k (uncased)\nSeed 0 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1300k (uncased)
Seed 0 intermediate checkpoint 1300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1300k | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
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] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 1300k (uncased)
Seed 0 intermediate checkpoint 1300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1300k (uncased)\nSeed 0 intermediate checkpoint 1300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1400k (uncased)
Seed 0 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1400k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
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"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 1400k (uncased)
Seed 0 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1400k (uncased)\nSeed 0 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 0 Checkpoint 140k (uncased)
Seed 0 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-140k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 140k (uncased)
Seed 0 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 140k (uncased)\nSeed 0 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1500k (uncased)
Seed 0 intermediate checkpoint 1500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1500k | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 1500k (uncased)
Seed 0 intermediate checkpoint 1500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1500k (uncased)\nSeed 0 intermediate checkpoint 1500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1600k (uncased)
Seed 0 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1600k | null | [
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"arxiv:2106.16163",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-0 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 0 Checkpoint 1600k (uncased)
Seed 0 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1600k (uncased)\nSeed 0 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 160k (uncased)
Seed 0 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-160k | null | [
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| # MultiBERTs Seed 0 Checkpoint 160k (uncased)
Seed 0 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 160k (uncased)\nSeed 0 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1700k (uncased)
Seed 0 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1700k | null | [
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| # MultiBERTs Seed 0 Checkpoint 1700k (uncased)
Seed 0 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1700k (uncased)\nSeed 0 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1800k (uncased)
Seed 0 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1800k | null | [
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| # MultiBERTs Seed 0 Checkpoint 1800k (uncased)
Seed 0 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1800k (uncased)\nSeed 0 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 180k (uncased)
Seed 0 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-180k | null | [
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| # MultiBERTs Seed 0 Checkpoint 180k (uncased)
Seed 0 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 180k (uncased)\nSeed 0 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 1900k (uncased)
Seed 0 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-1900k | null | [
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| # MultiBERTs Seed 0 Checkpoint 1900k (uncased)
Seed 0 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 1900k (uncased)\nSeed 0 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 2000k (uncased)
Seed 0 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-2000k | null | [
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| # MultiBERTs Seed 0 Checkpoint 2000k (uncased)
Seed 0 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 0 Checkpoint 2000k (uncased)\nSeed 0 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 200k (uncased)
Seed 0 intermediate checkpoint 200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-200k | null | [
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| # MultiBERTs Seed 0 Checkpoint 200k (uncased)
Seed 0 intermediate checkpoint 200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 200k (uncased)\nSeed 0 intermediate checkpoint 200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 20k (uncased)
Seed 0 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-20k | null | [
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| # MultiBERTs Seed 0 Checkpoint 20k (uncased)
Seed 0 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
"# MultiBERTs Seed 0 Checkpoint 20k (uncased)\nSeed 0 intermediate checkpoint 20k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpoin... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 300k (uncased)
Seed 0 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-300k | null | [
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| # MultiBERTs Seed 0 Checkpoint 300k (uncased)
Seed 0 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 300k (uncased)\nSeed 0 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 400k (uncased)
Seed 0 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-400k | null | [
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| # MultiBERTs Seed 0 Checkpoint 400k (uncased)
Seed 0 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 400k (uncased)\nSeed 0 intermediate checkpoint 400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 40k (uncased)
Seed 0 intermediate checkpoint 40k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-40k | null | [
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| # MultiBERTs Seed 0 Checkpoint 40k (uncased)
Seed 0 intermediate checkpoint 40k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 500k (uncased)
Seed 0 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-500k | null | [
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| # MultiBERTs Seed 0 Checkpoint 500k (uncased)
Seed 0 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 600k (uncased)
Seed 0 intermediate checkpoint 600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-600k | null | [
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| # MultiBERTs Seed 0 Checkpoint 600k (uncased)
Seed 0 intermediate checkpoint 600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 60k (uncased)
Seed 0 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-60k | null | [
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| # MultiBERTs Seed 0 Checkpoint 60k (uncased)
Seed 0 intermediate checkpoint 60k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 700k (uncased)
Seed 0 intermediate checkpoint 700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-700k | null | [
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| # MultiBERTs Seed 0 Checkpoint 700k (uncased)
Seed 0 intermediate checkpoint 700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 700k (uncased)\nSeed 0 intermediate checkpoint 700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 800k (uncased)
Seed 0 intermediate checkpoint 800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-800k | null | [
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| # MultiBERTs Seed 0 Checkpoint 800k (uncased)
Seed 0 intermediate checkpoint 800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 0 Checkpoint 800k (uncased)\nSeed 0 intermediate checkpoint 800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 80k (uncased)
Seed 0 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/goog... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-80k | null | [
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| # MultiBERTs Seed 0 Checkpoint 80k (uncased)
Seed 0 intermediate checkpoint 80k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be ... | [
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null | transformers | # MultiBERTs Seed 0 Checkpoint 900k (uncased)
Seed 0 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-0"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0-900k | null | [
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| # MultiBERTs Seed 0 Checkpoint 900k (uncased)
Seed 0 intermediate checkpoint 900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
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null | transformers | # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google-research/language/tree/master/language/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-0 | null | [
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| # MultiBERTs Seed 0 (uncased)
Seed 0 MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team re... | [
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null | transformers | # MultiBERTs Seed 1 Checkpoint 0k (uncased)
Seed 1 intermediate checkpoint 0k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/google... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-0k | null | [
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| # MultiBERTs Seed 1 Checkpoint 0k (uncased)
Seed 1 intermediate checkpoint 0k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can be fo... | [
"# MultiBERTs Seed 1 Checkpoint 0k (uncased)\nSeed 1 intermediate checkpoint 0k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpoint ... | [
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1000k (uncased)
Seed 1 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1000k | null | [
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| # MultiBERTs Seed 1 Checkpoint 1000k (uncased)
Seed 1 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1000k (uncased)\nSeed 1 intermediate checkpoint 1000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 1 Checkpoint 100k (uncased)
Seed 1 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-100k | null | [
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| # MultiBERTs Seed 1 Checkpoint 100k (uncased)
Seed 1 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 1 Checkpoint 100k (uncased)\nSeed 1 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1100k (uncased)
Seed 1 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1100k | null | [
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#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1100k (uncased)
Seed 1 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1100k (uncased)\nSeed 1 intermediate checkpoint 1100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1200k (uncased)
Seed 1 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1200k | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1200k (uncased)
Seed 1 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1200k (uncased)\nSeed 1 intermediate checkpoint 1200k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 120k (uncased)
Seed 1 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-120k | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 120k (uncased)
Seed 1 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 1 Checkpoint 120k (uncased)\nSeed 1 intermediate checkpoint 120k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1300k (uncased)
Seed 1 intermediate checkpoint 1300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1300k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1300k (uncased)
Seed 1 intermediate checkpoint 1300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1300k (uncased)\nSeed 1 intermediate checkpoint 1300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1400k (uncased)
Seed 1 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1400k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1400k (uncased)
Seed 1 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1400k (uncased)\nSeed 1 intermediate checkpoint 1400k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
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null | transformers | # MultiBERTs Seed 1 Checkpoint 140k (uncased)
Seed 1 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-140k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 140k (uncased)
Seed 1 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 1 Checkpoint 140k (uncased)\nSeed 1 intermediate checkpoint 140k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1500k (uncased)
Seed 1 intermediate checkpoint 1500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1500k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
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] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1500k (uncased)
Seed 1 intermediate checkpoint 1500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1500k (uncased)\nSeed 1 intermediate checkpoint 1500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1600k (uncased)
Seed 1 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1600k | null | [
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"en"
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| # MultiBERTs Seed 1 Checkpoint 1600k (uncased)
Seed 1 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1600k (uncased)\nSeed 1 intermediate checkpoint 1600k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
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null | transformers | # MultiBERTs Seed 1 Checkpoint 160k (uncased)
Seed 1 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-160k | null | [
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"en"
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#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 160k (uncased)
Seed 1 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 1 Checkpoint 160k (uncased)\nSeed 1 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 1 Checkpoint 160k (uncased)\nSeed 1 intermediate checkpoint 160k MultiBERTs (pretrained ... | [
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null | transformers | # MultiBERTs Seed 1 Checkpoint 1700k (uncased)
Seed 1 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1700k | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1700k (uncased)
Seed 1 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1700k (uncased)\nSeed 1 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 1 Checkpoint 1700k (uncased)\nSeed 1 intermediate checkpoint 1700k MultiBERTs (pretraine... | [
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"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 1 Checkpoint 1700k (uncased)\nSeed 1 intermediate checkpoint 1700k MultiBERTs (pretrained BERT... |
null | transformers | # MultiBERTs Seed 1 Checkpoint 1800k (uncased)
Seed 1 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1800k | null | [
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"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1800k (uncased)
Seed 1 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1800k (uncased)\nSeed 1 intermediate checkpoint 1800k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 1 Checkpoint 1800k (uncased)\nSeed 1 intermediate checkpoint 1800k MultiBERTs (pretraine... | [
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126,
307,
110,
27,
80,
42,
4,
208,
116,
38
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"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 1 Checkpoint 1800k (uncased)\nSeed 1 intermediate checkpoint 1800k MultiBERTs (pretrained BERT... |
null | transformers | # MultiBERTs Seed 1 Checkpoint 180k (uncased)
Seed 1 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-180k | null | [
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"pytorch",
"bert",
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"multiberts",
"multiberts-seed-1",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 180k (uncased)
Seed 1 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can b... | [
"# MultiBERTs Seed 1 Checkpoint 180k (uncased)\nSeed 1 intermediate checkpoint 180k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final checkpo... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 1 Checkpoint 180k (uncased)\nSeed 1 intermediate checkpoint 180k MultiBERTs (pretrained ... | [
71,
126,
307,
110,
27,
80,
42,
4,
208,
116,
38
] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 1 Checkpoint 180k (uncased)\nSeed 1 intermediate checkpoint 180k MultiBERTs (pretrained BERT) ... |
null | transformers | # MultiBERTs Seed 1 Checkpoint 1900k (uncased)
Seed 1 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/... | {"language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"], "datasets": ["bookcorpus", "wikipedia"]} | MultiBertGunjanPatrick/multiberts-seed-1-1900k | null | [
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"multiberts-seed-1",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2106.16163"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us
| # MultiBERTs Seed 1 Checkpoint 1900k (uncased)
Seed 1 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This is an intermediate checkpoint.
The final checkpoint can... | [
"# MultiBERTs Seed 1 Checkpoint 1900k (uncased)\nSeed 1 intermediate checkpoint 1900k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This is an intermediate checkpoint.\nThe final check... | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# MultiBERTs Seed 1 Checkpoint 1900k (uncased)\nSeed 1 intermediate checkpoint 1900k MultiBERTs (pretraine... | [
71,
126,
307,
110,
27,
80,
42,
4,
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116,
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] | [
"TAGS\n#transformers #pytorch #bert #pretraining #exbert #multiberts #multiberts-seed-1 #en #dataset-bookcorpus #dataset-wikipedia #arxiv-2106.16163 #license-apache-2.0 #endpoints_compatible #region-us \n# MultiBERTs Seed 1 Checkpoint 1900k (uncased)\nSeed 1 intermediate checkpoint 1900k MultiBERTs (pretrained BERT... |
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