modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 1,907 | null | # StackOBERTflow-comments-small
StackOBERTflow is a RoBERTa model trained on StackOverflow comments.
A Byte-level BPE tokenizer with dropout was used (using the `tokenizers` package).
The model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens.
The model was trained for ... | [
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0.026620367541909218,
0.03959959000349045,
0.02629942260682583,
-0.011815739795565605,
0.018889382481575012,
-0.026951011270284653,
0.037962693721055984,
0.042749688029289246,
-0.010047368705272675,
0.008536167442798615,
... |
Dandara/bertimbau-socioambiental | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"no_rep... | 27 | null | ---
language: fr
widget:
- text: "Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages"
---
## About
The *french-camembert-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset availab... | [
-0.01319242361932993,
-0.010987607762217522,
-0.0021315342746675014,
0.03358825296163559,
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0.02171473577618599,
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0.05080987140536308,
-0.00908660888671875,
-0.014171392656862736,
-0.009275420568883419,
0... |
Danih1502/t5-base-finetuned-en-to-de | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ## About
The *french-postag-model* is a part of speech tagging model for French that was trained on the *free-french-treebank* dataset available on
[github](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'bert-base-multilingual-cased'*.
## Supported Tag... | [
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0.0345778726041317,
0.03840886056423187,
0.02252705581486225,
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0.043541792780160904,
-0.005394854582846165,
-0.018537597730755806,
-0.00895595084875822,
0.... |
Danih1502/t5-small-finetuned-en-to-de | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | null | ---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- The Pile
---
# GPT-J 6B
## Model Description
GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represent... | [
-0.04004959017038345,
-0.005944880656898022,
0.006442598067224026,
0.038142237812280655,
0.035297419875860214,
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0.01711977645754814,
0.01104937493801117,
-0.01839999482035637,
0.04117236286401749,
0.016388684511184692,
-0.02693876251578331,
0.006749332416802645,
0.02707... |
DarkWolf/kn-electra-small | [
"pytorch",
"electra",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": ... | 4 | null | ---
tags:
- conversational
---
# Jake Peralta DialoGPT model | [
-0.046232011169195175,
0.017776332795619965,
0.019810473546385765,
0.014691066928207874,
0.017069721594452858,
0.0017648331122472882,
0.000621126324404031,
0.02983774058520794,
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0.02154335007071495,
0.03458734601736069,
-0.047308001667261124,
0.012191777117550373,
0.0... |
Darkrider/covidbert_medmarco | [
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:2010.05987",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 35 | null | # cse_resnet50
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tri... | [
-0.010881291702389717,
-0.003975145984441042,
-0.02389795333147049,
0.007638996466994286,
0.04135437682271004,
0.01465374231338501,
-0.01820448599755764,
0.0020712187979370356,
-0.025089871138334274,
0.06721970438957214,
0.021264176815748215,
0.011266675777733326,
0.02266835980117321,
0.03... |
Darkrider/covidbert_mednli | [
"transformers"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 3 | null | # deit_base_patch16_224
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://githu... | [
-0.01907229796051979,
-0.017780596390366554,
0.002614076714962721,
-0.004866929724812508,
0.02473740465939045,
0.027285216376185417,
-0.008107730187475681,
-0.018411364406347275,
-0.018270637840032578,
0.05078576132655144,
0.03529519960284233,
-0.015149877406656742,
0.026203172281384468,
0... |
Darren/darren | [
"pytorch"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | # deit_base_patch16_384
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://githu... | [
-0.018664147704839706,
-0.018005531281232834,
0.001881765783764422,
-0.005595056340098381,
0.0240417942404747,
0.028673553839325905,
-0.008018208667635918,
-0.01887533813714981,
-0.019151821732521057,
0.05052749067544937,
0.0351269356906414,
-0.015178904868662357,
0.026392986997961998,
0.0... |
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 14 | null | # deit_small_patch16_224
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://gith... | [
-0.01417841762304306,
-0.014423005282878876,
0.0015375789953395724,
-0.0028741874266415834,
0.027323056012392044,
0.02383936010301113,
-0.00929131917655468,
-0.018083222210407257,
-0.01807132177054882,
0.05606764927506447,
0.02996056340634823,
-0.01177921798080206,
0.024363072589039803,
0.... |
Darya/layoutlmv2-finetuned-funsd-test | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | # deit_tiny_patch16_224
Implementation of DeiT proposed in [Training data-efficient image
transformers & distillation through
attention](https://arxiv.org/pdf/2010.11929.pdf)
An attention based distillation is proposed where a new token is added
to the model, the [dist]{.title-ref} token.
![image](https://githu... | [
-0.016678711399435997,
-0.012443575076758862,
0.0036706801038235426,
-0.0037700904067605734,
0.027386384084820747,
0.02158581092953682,
-0.011121436022222042,
-0.018078532069921494,
-0.016887150704860687,
0.05730925500392914,
0.030168354511260986,
-0.011535000056028366,
0.02533126063644886,
... |
DataikuNLP/TinyBERT_General_4L_312D | [
"pytorch",
"jax",
"bert",
"arxiv:1909.10351",
"transformers"
] | null | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 74 | null | # densenet161
Implementation of DenseNet proposed in [Densely Connected Convolutional
Networks](https://arxiv.org/abs/1608.06993)
Create a default models
``` {.sourceCode .}
DenseNet.densenet121()
DenseNet.densenet161()
DenseNet.densenet169()
DenseNet.densenet201()
```
Examples:
``` {.sourceCode .}
# ch... | [
-0.058146629482507706,
-0.009660565294325352,
-0.0035529807209968567,
0.03416043147444725,
0.03777407854795456,
0.023282725363969803,
-0.012581013143062592,
0.004692208953201771,
-0.020358750596642494,
0.0680939182639122,
0.03955395147204399,
0.006599499844014645,
0.05099461227655411,
0.04... |
DataikuNLP/camembert-base | [
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_... | 8 | null | # densenet201
Implementation of DenseNet proposed in [Densely Connected Convolutional
Networks](https://arxiv.org/abs/1608.06993)
Create a default models
``` {.sourceCode .}
DenseNet.densenet121()
DenseNet.densenet161()
DenseNet.densenet169()
DenseNet.densenet201()
```
Examples:
``` {.sourceCode .}
# ch... | [
-0.056471407413482666,
-0.009409385733306408,
-0.00241450360044837,
0.03472254052758217,
0.03712563216686249,
0.024956153705716133,
-0.012902970425784588,
0.004467745777219534,
-0.02092227339744568,
0.06776954233646393,
0.039778292179107666,
0.00704854354262352,
0.05280386283993721,
0.0469... |
DataikuNLP/distiluse-base-multilingual-cased-v1 | [
"pytorch",
"distilbert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 29 | null | # ResNet
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
ResNet.resnet152()
ResNet.resnet200()
Variants (d) proposed in `Bag of Tricks fo... | [
-0.015460851602256298,
-0.004170822445303202,
-0.023213276639580727,
0.007667414378374815,
0.04199064150452614,
0.01661006174981594,
-0.014681111089885235,
0.0013295921962708235,
-0.021788274869322777,
0.06998997926712036,
0.02060845121741295,
0.010446575470268726,
0.025040777400135994,
0.... |
DataikuNLP/paraphrase-albert-small-v2 | [
"pytorch",
"albert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
"architectures": [
"AlbertModel"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size":... | 628 | null | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet
---
# eca_resnet26t
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()... | [
-0.012329756282269955,
-0.011966632679104805,
-0.012687774375081062,
0.012402238324284554,
0.052951630204916,
-0.002878142287954688,
-0.01743188127875328,
0.010705671273171902,
-0.02012714184820652,
0.07531248033046722,
0.021242856979370117,
0.013574273325502872,
0.02434656023979187,
0.038... |
DavidAMcIntosh/DialoGPT-small-rick | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | # efficientnet_b0
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | [
-0.03285360708832741,
0.00008798094495432451,
-0.01105810422450304,
0.005660280119627714,
-0.002349477494135499,
0.03495285287499428,
-0.026654386892914772,
-0.007434857077896595,
-0.01833554357290268,
0.031448449939489365,
0.012532512657344341,
0.0035737587604671717,
0.03262569382786751,
... |
DavidSpaceG/MSGIFSR | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | # efficientnet_b2
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | [
-0.030359657481312752,
0.0008279521134682,
-0.010456696152687073,
0.0063459197990596294,
-0.0026872430462390184,
0.03444882854819298,
-0.025775622576475143,
-0.006975637748837471,
-0.017828918993473053,
0.03074171394109726,
0.013140846975147724,
0.0059840502217411995,
0.03183060139417648,
... |
Davlan/bert-base-multilingual-cased-finetuned-amharic | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 109 | null | # efficientnet_b3
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | [
-0.03253432363271713,
-0.001534161390736699,
-0.01266905665397644,
0.0038330592215061188,
-0.0011920948745682836,
0.03592173010110855,
-0.028132952749729156,
-0.009019823744893074,
-0.017968887463212013,
0.0330875888466835,
0.014811994507908821,
0.002918670419603586,
0.029906198382377625,
... |
Davlan/bert-base-multilingual-cased-finetuned-hausa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 151 | null | # efficientnet_b6
Implementation of EfficientNet proposed in [EfficientNet: Rethinking
Model Scaling for Convolutional Neural
Networks](https://arxiv.org/abs/1905.11946)
The basic architecture ... | [
-0.03343190997838974,
0.00017264187044929713,
-0.0070616756565868855,
0.00452214851975441,
-0.0018717477796599269,
0.030772820115089417,
-0.026243770495057106,
-0.010316590778529644,
-0.01955443248152733,
0.029612388461828232,
0.012351227924227715,
0.001140748499892652,
0.03200516104698181,
... |
Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 27 | null | # regnetx_002
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | [
-0.010898878797888756,
0.002207833807915449,
-0.036054663360118866,
0.006085923407226801,
0.007846992462873459,
0.025368550792336464,
-0.01956082694232464,
0.0055623045191168785,
-0.02137119509279728,
0.033087316900491714,
0.01012002769857645,
0.00972064584493637,
0.03702361136674881,
0.03... |
Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | # regnetx_006
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | [
-0.012861737050116062,
0.003339825663715601,
-0.03433955833315849,
0.0062171160243451595,
0.006959756836295128,
0.02298617549240589,
-0.018714439123868942,
0.003825426334515214,
-0.02238219603896141,
0.03129195421934128,
0.009828462265431881,
0.006067828740924597,
0.03790481016039848,
0.02... |
Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 67 | null | # regnetx_016
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | [
-0.011923564597964287,
0.0026358815375715494,
-0.03725734353065491,
0.0064923823811113834,
0.008032000623643398,
0.02478671446442604,
-0.01857266016304493,
0.005908479914069176,
-0.02208833582699299,
0.03281652554869652,
0.01026789378374815,
0.007263926789164543,
0.038318537175655365,
0.03... |
Davlan/bert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 269,898 | null | # regnety_002
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | [
-0.011483919806778431,
0.002645461354404688,
-0.03479751944541931,
0.006015956401824951,
0.008874326944351196,
0.023954231292009354,
-0.017047571018338203,
0.008791008032858372,
-0.025191863998770714,
0.03325800597667694,
0.009933865629136562,
0.010189964435994625,
0.038955122232437134,
0.... |
Davlan/distilbert-base-multilingual-cased-masakhaner | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"arxiv:2103.11811",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 16 | null | # regnety_008
Implementation of RegNet proposed in [Designing Network Design
Spaces](https://arxiv.org/abs/2003.13678)
The main idea is to start with a high dimensional search space and
iteratively reduce the search space by empirically apply constrains
based on the best performing models sampled by the current sea... | [
-0.013392970897257328,
0.004442110192030668,
-0.03488373011350632,
0.006161899771541357,
0.008160284720361233,
0.023767294362187386,
-0.015628712251782417,
0.006786175072193146,
-0.024812577292323112,
0.03358060494065285,
0.009718550369143486,
0.008532579988241196,
0.041188254952430725,
0.... |
Davlan/xlm-roberta-base-finetuned-amharic | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 401 | null | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet
---
# resnet50
Implementation of ResNet proposed in [Deep Residual Learning for Image
Recognition](https://arxiv.org/abs/1512.03385)
``` python
ResNet.resnet18()
ResNet.resnet26()
ResNet.resnet34()
ResNet.resnet50()
ResNet.resnet101()
Res... | [
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0.02331315167248249,
0.0... |
Davlan/xlm-roberta-base-ner-hrl | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
... | 760 | null | # vit_large_patch16_224
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/gla... | [
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0.009101212956011295,
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0.061817917972803116,
0.039050690829753876,
0.002447753679007292,
0.013797975145280361,
... |
Davlan/xlm-roberta-base-sadilar-ner | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 12 | null | # vit_large_patch16_384
Implementation of Vision Transformer (ViT) proposed in [An Image Is
Worth 16x16 Words: Transformers For Image Recognition At
Scale](https://arxiv.org/pdf/2010.11929.pdf)
The following image from the authors shows the architecture.
![image](https://github.com/FrancescoSaverioZuppichini/gla... | [
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0.062036946415901184,
0.03861137479543686,
0.002148775849491358,
0.01493845134973526,
0.... |
Davlan/xlm-roberta-base-wikiann-ner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 235 | 2021-04-09T16:55:08Z | # wide_resnet101_2
Implementation of Wide ResNet proposed in [\"Wide Residual
Networks\"](https://arxiv.org/pdf/1605.07146.pdf)
Create a default model
``` python
WideResNet.wide_resnet50_2()
WideResNet.wide_resnet101_2()
# create a wide_resnet18_4
WideResNet.resnet18(block=WideResNetBottleNeckBlock, width_facto... | [
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0.028131110593676567,
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0.06252355128526688,
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0.03503497317433357,
... |
Dazai/Ko | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- "es"
- "robust-speech-event"
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probab... | [
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0... |
Dazai/Ok | [] | null | {
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},
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"num_beams... | 0 | null | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-es-test-lm
results:
- task:
name: Automatic Spee... | [
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0... |
DeBERTa/deberta-v2-xxlarge | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
language: ro
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Romanian by George Mihaila
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name:... | [
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0... |
DeadBeast/korscm-mBERT | [
"pytorch",
"bert",
"text-classification",
"korean",
"dataset:Korean-Sarcasm",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 43 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: BERiTmodel2
results: []
---
<!-- 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. -->
# BERiTmodel2
This model is a f... | [
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... |
DeadBeast/marathi-roberta-base | [] | null | {
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},
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
... | [
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0... |
DeadBeast/mbert-base-cased-finetuned-bengali-fakenews | [
"pytorch",
"bert",
"text-classification",
"bengali",
"dataset:BanFakeNews",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 37 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
... | [
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0.05008528009057045,
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0... |
DeadBeast/roberta-base-pretrained-mr-2 | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngra... | 5 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: diam
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9775280952453613
---
# diam
Autogenerated by Huggi... | [
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0.0... |
DeadBeast/roberta-base-pretrained-mr | [
"jax",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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"min_length": null,
"no_repeat_ngra... | 6 | null | ---
language: ko
tags:
- bart
license: mit
---
## KoBART-base-v1
```python
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v1')
model = BartModel.from_pretrained('gogamza/kobart-base-v1')
```
| [
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0.038... |
Dean/summarsiation | [] | null | {
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"num_beams... | 0 | null | ---
language: ko
license: mit
tags:
- bart
---
# Model Card for kobart-base-v2
# Model Details
## Model Description
[**BART**](https://arxiv.org/pdf/1910.13461.pdf)(**B**idirectional and **A**uto-**R**egressive **T**ransformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 `autoencoder`의 형태로 학습이 됩니다. 한국어 BART(이하 **K... | [
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DecafNosebleed/DialoGPT-small-ScaraBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size... | 15 | null | ---
language: ko
tags:
- bart
license: mit
---
# Korean News Summarization Model
## Demo
https://huggingface.co/spaces/gogamza/kobart-summarization
## How to use
```python
import torch
from transformers import PreTrainedTokenizerFast
from transformers import BartForConditionalGeneration
tokenizer = PreTrainedToke... | [
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"num_beams... | 0 | null | Please refer : https://github.com/haven-jeon/LegalQA#train | [
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DecafNosebleed/scarabot-model | [
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language:
- ain
- ja
tags:
- translation
---
Byt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
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Declan/Breitbart_model_v1 | [
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"no_repeat_ngram_size... | 9 | null | ---
language:
- jpn
- ain
tags:
- translation
---
Byt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's ByT5-small](https://huggingface.co/google/byt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
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"no_repeat_ngram_size... | 7 | null | ---
language:
- jpn
- ain
tags:
- translation
---
mt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
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Declan/Breitbart_model_v3 | [
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"no_repeat_ngram_size... | 7 | null | ---
language:
- jpn
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tags:
- translation
---
mt5-small-jpn-ain-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Japanese to Ainu language.
| [
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Declan/Breitbart_model_v4 | [
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"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remov... | [
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Declan/ChicagoTribune_model_v1 | [
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language:
- en
- de
license: apache-2.0
datasets:
- wmt14
tags:
- translation
---
# bert2bert_L-24_wmt_de_en EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev... | [
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language:
- en
- de
license: apache-2.0
datasets:
- wmt14
tags:
- translation
---
# bert2bert_L-24_wmt_en_de EncoderDecoder model
The model was introduced in
[this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev... | [
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"no_repeat_ngram_size... | 7 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
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Declan/ChicagoTribune_model_v5 | [
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"no_repeat_ngram_size... | 7 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
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Declan/ChicagoTribune_model_v6 | [
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"no_repeat_ngram_size... | 5 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
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Declan/ChicagoTribune_model_v7 | [
"pytorch",
"bert",
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"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 7 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
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Declan/ChicagoTribune_model_v8 | [
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"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 7 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
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Declan/FoxNews_model_v4 | [
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"autotrain_compatible"
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"no_repeat_ngram_size... | 7 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
BERT Miniatures
===
This is the set of 24 BERT models referenced in [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962) (English only, uncased, trained with Word... | [
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Declan/NewYorkTimes_model_v8 | [
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"no_repeat_ngram_size... | 3 | null | ---
language:
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Declan/Politico_model_v1 | [
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language:
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Declan/Politico_model_v2 | [
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] | fill-mask | {
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"no_repeat_ngram_size... | 5 | null | ---
language:
- multilingual
- af
- sq
- ar
- an
- hy
- ast
- az
- ba
- eu
- bar
- be
- bn
- inc
- bs
- br
- bg
- my
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- ceb
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- zh
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- mk... | [
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Declan/Politico_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 5 | null | ---
language:
- multilingual
- af
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- hy
- ast
- az
- ba
- eu
- bar
- be
- bn
- inc
- bs
- br
- bg
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- ceb
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- cv
- hr
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- et
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Declan/Reuters_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 5 | null | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/google.png
license: apache-2.0
---
**WARNING**: This is the official generator checkpoint as in the [ELECTRA original codebase](https://github.com/google-research/electra). However, this model is not scaled properly for pre-training with [google/elec... | [
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Declan/Reuters_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 7 | null | ---
language: en
tags:
- fnet
license: apache-2.0
datasets:
- c4
---
# FNet base model
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository](... | [
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Declan/Reuters_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 3 | null | ---
language: en
tags:
- fnet
license: apache-2.0
datasets:
- c4
---
# FNet large model
Pretrained model on English language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was
introduced in [this paper](https://arxiv.org/abs/2105.03824) and first released in [this repository]... | [
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Declan/test_model | [] | null | {
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language: en
tags:
- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_1200k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
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Declan/test_push | [] | null | {
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language: en
tags:
- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_120k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 120k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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0.0... |
DeepChem/ChemBERTa-5M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
],
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"no_repeat_ngra... | 29 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_1500k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
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DeepPavlov/bert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
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"no_repeat_ngram_size": nul... | 3,009 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_200k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | {
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"n... | 6,324 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_300k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.01949971541762352,
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0.0053948224522173405,
0.0468... |
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | {
"architectures": null,
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},
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"n... | 9,141 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_0
- multiberts-seed_0-step_400k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 0, Step 400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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0.04702... |
Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"min_length": null,
"no_repeat_ngram_size... | 14 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_1400k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
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0.047... |
Deniskin/essays_small_2000 | [] | null | {
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},
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"num_beams... | 0 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_140k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 140k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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DeskDown/MarianMixFT_en-hi | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_180k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 180k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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0.046... |
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
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"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_1900k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
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0.0... |
DeskDown/MarianMixFT_en-ja | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | 2021-11-06T01:20:28Z | ---
language: en
tags:
- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_2000k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.019379829987883568,
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0.... |
DeskDown/MarianMixFT_en-ms | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
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"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_1
- multiberts-seed_1-step_200k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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0.004638202954083681,
0.04... |
DewiBrynJones/wav2vec2-large-xlsr-welsh | [
"cy",
"dataset:common_voice",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": null,
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"num_beams... | 0 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_11
license: apache-2.0
---
# MultiBERTs - Seed 11
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://... | [
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0.03455149009823799,
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0.0062120151706039906,
0.04... |
Dibyaranjan/nl_image_search | [] | null | {
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},
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"num_beams... | 0 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_16
license: apache-2.0
---
# MultiBERTs - Seed 16
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://... | [
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0.... |
DiegoBalam12/institute_classification | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_19
license: apache-2.0
---
# MultiBERTs - Seed 19
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://... | [
-0.017877716571092606,
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0.006556046660989523,
0.0439... |
Digakive/Hsgshs | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | 2021-11-06T01:22:03Z | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_0k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 0k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
... | [
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0.052554383873939514,
0.03383956104516983,
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0.... |
Dilmk2/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 13 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_1000k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
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0... |
DimaOrekhov/cubert-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 10 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_100k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 100k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
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0.05291897431015968,
0.034526851028203964,
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0.0033359031658619642,
0... |
Donghyun/L2_BERT | [] | null | {
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},
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"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_2000k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
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0.05269318073987961,
0.03463568165898323,
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0.0035196682438254356,
0... |
Waynehillsdev/Wayne_NLP_mT5 | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 11 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_300k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020154446363449097,
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-0.030777333304286003,
0.04979512840509415,
0.023412000387907028,
0.04021058604121208,
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0.05300912633538246,
0.034540798515081406,
-0.028246905654668808,
0.0032974036876112223,
... |
Doogie/Waynehills-KE-T5-doogie | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_400k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 400k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020062804222106934,
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0.049703579396009445,
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0.052894819527864456,
0.03406617417931557,
-0.028028951957821846,
0.0033022924326360226,
... |
Waynehillsdev/Waynehills-STT-doogie-server | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 61 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_40k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 40k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained wit... | [
-0.020661087706685066,
-0.007669155485928059,
-0.03057413548231125,
0.04961792007088661,
0.023341890424489975,
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0.008546795696020126,
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0.05272013694047928,
0.03517832234501839,
-0.027784869074821472,
0.0031439617741853,
0.047... |
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 5 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_500k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 500k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020217139273881912,
-0.007756567094475031,
-0.030138887465000153,
0.04981480911374092,
0.02302391454577446,
0.04022751376032829,
0.007737635634839535,
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0.05265611782670021,
0.034472569823265076,
-0.027886899188160896,
0.0034687193110585213,
0.0... |
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 5 | 2021-11-06T01:45:48Z | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_600k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020134001970291138,
-0.007820552214980125,
-0.030540743842720985,
0.049949340522289276,
0.023159680888056755,
0.040068257600069046,
0.007927581667900085,
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0.05281342193484306,
0.0342797115445137,
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0.003781761508435011,
0.... |
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 33 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_60k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 60k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained wit... | [
-0.02006935141980648,
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0.04999990016222,
0.023200957104563713,
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0.007934297434985638,
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0.05269308015704155,
0.034358203411102295,
-0.028208840638399124,
0.00331450835801661,
0.0473... |
Doohae/p_encoder | [
"pytorch"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 3 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_700k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020454691722989082,
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-0.03029778227210045,
0.049878623336553574,
0.02336699329316616,
0.04020541161298752,
0.007931345142424107,
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0.05265900865197182,
0.034645628184080124,
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0.0038639497943222523,
0.0... |
Doohae/q_encoder | [
"pytorch"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 3 | 2021-11-06T01:49:11Z | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_800k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020407244563102722,
-0.007616731338202953,
-0.03062593564391136,
0.049791865050792694,
0.02308989316225052,
0.0398995541036129,
0.007688541896641254,
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0.05312640964984894,
0.03454391658306122,
-0.028301816433668137,
0.003718645079061389,
0.047... |
Doohae/roberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 3 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_80k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 80k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained wit... | [
-0.020419994369149208,
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0.05008714646100998,
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0.05265139043331146,
0.0342993400990963,
-0.028146563097834587,
0.0032430277206003666,
0.... |
Doquey/DialoGPT-small-Luisbot1 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
- multiberts-seed_2-step_900k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 2, Step 900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020101504400372505,
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0.04938085004687309,
0.02316037379205227,
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0.053117044270038605,
0.033698245882987976,
-0.028287604451179504,
0.003753714496269822,
0.0... |
Doquey/DialoGPT-small-Michaelbot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_2
license: apache-2.0
---
# MultiBERTs - Seed 2
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained with
similar hyper-parameters as
[the original BERT model](https://gi... | [
-0.01668665185570717,
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0.041758157312870026,
0.0017200442962348461,
-0.02845938131213188,
-0.029567288234829903,
0.048525888472795486,
0.03325365483760834,
-0.02278534509241581,
0.005937276873737574,
0.0... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 37 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_120k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 120k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.021076848730444908,
-0.008238882757723331,
-0.03234953433275223,
0.04896458238363266,
0.02422938495874405,
0.042486608028411865,
0.008699845522642136,
-0.030815651640295982,
-0.028848771005868912,
0.053822390735149384,
0.03510397672653198,
-0.029688963666558266,
0.0027270005084574223,
0... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 33 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1300k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.024937152862548828,
-0.006648527458310127,
-0.030852070078253746,
0.04895204305648804,
0.02861209213733673,
0.040778860449790955,
0.006234021857380867,
-0.031247051432728767,
-0.028788859024643898,
0.05289534851908684,
0.03622014820575714,
-0.031083738431334496,
0.001413683407008648,
0.... |
DoyyingFace/bert-asian-hate-tweets-concat-clean | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 25 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1600k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1600k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.0206966120749712,
-0.00822963286191225,
-0.032385893166065216,
0.04909573495388031,
0.02441735565662384,
0.042694348841905594,
0.00839243084192276,
-0.03122248500585556,
-0.028886059299111366,
0.0537407360970974,
0.03517722710967064,
-0.02975546009838581,
0.0031516384333372116,
0.046227... |
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 38,156 | 2021-11-06T02:24:57Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_160k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 160k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.02087109163403511,
-0.008216005750000477,
-0.03221999481320381,
0.048554014414548874,
0.02453526481986046,
0.0424334742128849,
0.008271753787994385,
-0.030384546145796776,
-0.02900976501405239,
0.0538368821144104,
0.034651510417461395,
-0.029877914115786552,
0.003002887824550271,
0.0463... |
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 4,785,283 | 2021-11-06T02:54:29Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1700k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1700k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.025091474875807762,
-0.006887948140501976,
-0.03017965704202652,
0.04830939322710037,
0.028690537437796593,
0.04042189195752144,
0.006361143663525581,
-0.03108295425772667,
-0.029556771740317345,
0.05298159644007683,
0.0363275483250618,
-0.031302012503147125,
0.0011647408828139305,
0.04... |
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 687 | 2021-11-06T02:56:16Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1800k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1800k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.021415483206510544,
-0.008284448646008968,
-0.0318574458360672,
0.04820028319954872,
0.024253927171230316,
0.04232151806354523,
0.009028423577547073,
-0.030913982540369034,
-0.029077723622322083,
0.0539775975048542,
0.035171929746866226,
-0.02963237464427948,
0.003096138359978795,
0.046... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 26,792 | 2021-11-06T02:26:44Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_180k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 180k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.02103077620267868,
-0.00816881563514471,
-0.03210639953613281,
0.04835645481944084,
0.024380892515182495,
0.04211191460490227,
0.008517757058143616,
-0.030719874426722527,
-0.028767306357622147,
0.05389482155442238,
0.034956060349941254,
-0.030199049040675163,
0.002886488102376461,
0.04... |
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 341 | 2021-11-06T02:57:58Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_1900k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 1900k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.02109879069030285,
-0.008087973110377789,
-0.0317578949034214,
0.04804617911577225,
0.024088703095912933,
0.04218592494726181,
0.00904235802590847,
-0.03135756775736809,
-0.029686156660318375,
0.05399565026164055,
0.035197462886571884,
-0.030093805864453316,
0.002727448008954525,
0.0463... |
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 2,973 | 2021-11-06T02:59:45Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_2000k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 2000k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained... | [
-0.02104254812002182,
-0.007873537950217724,
-0.03204198554158211,
0.04844025894999504,
0.024364545941352844,
0.04220559075474739,
0.008772352710366249,
-0.030469905585050583,
-0.028827166184782982,
0.05385830998420715,
0.03535610809922218,
-0.029828296974301338,
0.0030004915315657854,
0.0... |
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 7,091 | 2021-11-06T02:28:25Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_200k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 200k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020517751574516296,
-0.008255068212747574,
-0.032303232699632645,
0.04929647594690323,
0.02407022751867771,
0.04230307415127754,
0.008500899188220501,
-0.0306706540286541,
-0.028736932203173637,
0.05366295948624611,
0.03487499803304672,
-0.029490869492292404,
0.0029438685160130262,
0.04... |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 42,640 | null | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_20k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 20k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained wit... | [
-0.020934296771883965,
-0.008249133825302124,
-0.032220181077718735,
0.04873161017894745,
0.023906055837869644,
0.042387042194604874,
0.009011651389300823,
-0.03064965456724167,
-0.02867189608514309,
0.053599946200847626,
0.035490673035383224,
-0.02961689606308937,
0.00268353708088398,
0.0... |
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11,644 | 2021-11-06T02:30:19Z | ---
language: en
tags:
- multiberts
- multiberts-seed_3
- multiberts-seed_3-step_300k
license: apache-2.0
---
# MultiBERTs, Intermediate Checkpoint - Seed 3, Step 300k
MultiBERTs is a collection of checkpoints and a statistical library to support
robust research on BERT. We provide 25 BERT-base models trained w... | [
-0.020903339609503746,
-0.008319158107042313,
-0.03231151029467583,
0.04846712946891785,
0.024164237082004547,
0.04195493832230568,
0.00842452235519886,
-0.031006919220089912,
-0.028731461614370346,
0.05415056645870209,
0.03529886528849602,
-0.03002096898853779,
0.0026626901235431433,
0.04... |
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