sha null | last_modified null | library_name stringclasses 154
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values | id stringlengths 5 122 | tags listlengths 1 1.84k | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 | tokens_length listlengths 1 723 | input_texts listlengths 1 61 | embeddings listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null | null | from transformers import GPTNeoForCausalLM, GPT2Tokenizer
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
... "previously ... | {} | null | Begimay/Task | [
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| from transformers import GPTNeoForCausalLM, GPT2Tokenizer
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
... "previously ... | [] | [
"TAGS\n#region-us \n"
] | [
6
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"passage: TAGS\n#region-us \n"
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0.03... |
null | null | transformers | \ntags:
-conversational
inference: false
conversational: true
#First time chat bot using a guide, low epoch count due to limited resources. | {} | text-generation | BenWitter/DialoGPT-small-Tyrion | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| \ntags:
-conversational
inference: false
conversational: true
#First time chat bot using a guide, low epoch count due to limited resources. | [] | [
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null | null | 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. -->
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colab", "results": []}]} | automatic-speech-recognition | Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
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null | null | 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. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi", "results": []}]} | automatic-speech-recognition | Bharathdamu/wav2vec2-large-xls-r-300m-hindi | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
... | [
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null | null | 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. -->
# roberta-base-finetuned-sst2
This model was trained from scratch on the glue dataset.
It achieves the following results on the ev... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "... | text-classification | Bhumika/roberta-base-finetuned-sst2 | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-finetuned-sst2
===========================
This model was trained from scratch on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3000
* Accuracy: 0.9450
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
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null | null | transformers |
# Spell checker using T5 base transformer
A simple spell checker built using T5-Base transformer. To use this model
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Bhuvana/t5-base-spellchecker")
model = AutoModelForSeq2SeqLM.from_pretrained("Bhuvana/t5-... | {"widget": [{"text": "christmas is celbrated on decembr 25 evry ear"}]} | text2text-generation | Bhuvana/t5-base-spellchecker | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Spell checker using T5 base transformer
A simple spell checker built using T5-Base transformer. To use this model
This should print the corrected statement
You can also type the text under the Hosted inference API and get predictions online.
| [
"# Spell checker using T5 base transformer\nA simple spell checker built using T5-Base transformer. To use this model \n\n\n\nThis should print the corrected statement\n\n\nYou can also type the text under the Hosted inference API and get predictions online."
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null | null | transformers | #hi | {"tags": ["conversational"]} | text-generation | Biasface/DDDC | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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null | null | transformers | #hi | {"tags": ["conversational"]} | text-generation | Biasface/DDDC2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers | ``````
!pip install transformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model = AutoModelForMaskedLM.from_pretrained("BigSalmon/BertaMyWorda")
`````` | {} | fill-mask | BigSalmon/BertaMyWorda | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
!pip install transformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model = AutoModelForMaskedLM.from_pretrained("BigSalmon/BertaMyWorda")
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null | null | transformers | https://huggingface.co/spaces/BigSalmon/MASK2 | {} | fill-mask | BigSalmon/FormalBerta3 | [
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"region:us"
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null | null | transformers | https://huggingface.co/spaces/BigSalmon/MASK2 | {} | fill-mask | BigSalmon/FormalRobertaa | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
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null | null | transformers | https://huggingface.co/spaces/BigSalmon/MASK2 | {} | fill-mask | BigSalmon/FormalRobertaaa | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: plea... | {} | text-generation | BigSalmon/GPTNeo350MInformalToFormalLincoln | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.0009209843119606376,
0.008442274294793606,
-0.004025787115097046,
-0.0005425113486126065,
0.1390179693698883,
0.025588439777493477,
0.04524734243750572,
0.14170925319194794,
-0.041948962956666946,
0.022018125280737877,
0.1525672823190689,
0.13523975014686584,
-0.03662027046084404,
0.142... |
null | null | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: plea... | {} | text-generation | BigSalmon/GPTNeo350MInformalToFormalLincoln2 | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.0009209843119606376,
0.008442274294793606,
-0.004025787115097046,
-0.0005425113486126065,
0.1390179693698883,
0.025588439777493477,
0.04524734243750572,
0.14170925319194794,
-0.041948962956666946,
0.022018125280737877,
0.1525672823190689,
0.13523975014686584,
-0.03662027046084404,
0.142... |
null | null | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLi... | {} | text-generation | BigSalmon/GPTNeo350MInformalToFormalLincoln3 | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.0009209843119606376,
0.008442274294793606,
-0.004025787115097046,
-0.0005425113486126065,
0.1390179693698883,
0.025588439777493477,
0.04524734243750572,
0.14170925319194794,
-0.041948962956666946,
0.022018125280737877,
0.1525672823190689,
0.13523975014686584,
-0.03662027046084404,
0.142... |
null | null | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLi... | {} | text-generation | BigSalmon/GPTNeo350MInformalToFormalLincoln4 | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.0009209843119606376,
0.008442274294793606,
-0.004025787115097046,
-0.0005425113486126065,
0.1390179693698883,
0.025588439777493477,
0.04524734243750572,
0.14170925319194794,
-0.041948962956666946,
0.022018125280737877,
0.1525672823190689,
0.13523975014686584,
-0.03662027046084404,
0.142... |
null | null | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln3")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLi... | {} | text-generation | BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.0009209843119606376,
0.008442274294793606,
-0.004025787115097046,
-0.0005425113486126065,
0.1390179693698883,
0.025588439777493477,
0.04524734243750572,
0.14170925319194794,
-0.041948962956666946,
0.022018125280737877,
0.1525672823190689,
0.13523975014686584,
-0.03662027046084404,
0.142... |
null | null | transformers | Trained on this model: https://huggingface.co/xhyi/PT_GPTNEO350_ATG/tree/main
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln6")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLi... | {} | text-generation | BigSalmon/GPTNeo350MInformalToFormalLincoln6 | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| Trained on this model: URL
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.0009209843119606376,
0.008442274294793606,
-0.004025787115097046,
-0.0005425113486126065,
0.1390179693698883,
0.025588439777493477,
0.04524734243750572,
0.14170925319194794,
-0.041948962956666946,
0.022018125280737877,
0.1525672823190689,
0.13523975014686584,
-0.03662027046084404,
0.142... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InfillFormalLincoln")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InfillFormalLincoln")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space c... | {} | text-generation | BigSalmon/InfillFormalLincoln | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
'
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln14")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln14")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for ... | {} | text-generation | BigSalmon/InformalToFormalLincoln14 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln15")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln15")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for ... | {} | text-generation | BigSalmon/InformalToFormalLincoln15 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
'
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln16")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln16")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for ... | {} | text-generation | BigSalmon/InformalToFormalLincoln16 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln17")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln17")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for ... | {} | text-generation | BigSalmon/InformalToFormalLincoln17 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln18")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln18")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for ... | {} | text-generation | BigSalmon/InformalToFormalLincoln18 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln19")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln19")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for ... | {} | text-generation | BigSalmon/InformalToFormalLincoln19 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
'
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
Wordy to Concise:
Fill Missing Phrase:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln20")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln20")
```
```
https://huggingface.c... | {} | text-generation | BigSalmon/InformalToFormalLincoln20 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
Wordy to Concise:
Fill Missing Phrase:
'
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
Wordy to Concise:
Fill Missing Phrase:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln21")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincoln21")
```
```
https://huggingface.c... | {} | text-generation | BigSalmon/InformalToFormalLincoln21 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Informal to Formal:
Wordy to Concise:
Fill Missing Phrase:
'
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
-0.010755456984043121,
0.024771401658654213,
-0.005105555523186922,
0.01467630174010992,
0.15965452790260315,
0.02063094452023506,
0.0777256190776825,
0.1480122059583664,
-0.030085699632763863,
0.009761088527739048,
0.16861428320407867,
0.16368600726127625,
-0.018452445045113564,
0.0991965... |
null | null | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln22")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln22")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tr... | {} | text-generation | BigSalmon/InformalToFormalLincoln22 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... | |
null | null | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln23")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln23")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tr... | {} | text-generation | BigSalmon/InformalToFormalLincoln23 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... | |
null | null | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln24")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln24")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tr... | {} | text-generation | BigSalmon/InformalToFormalLincoln24 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
-0.010755456984043121,
0.024771401658654213,
-0.005105555523186922,
0.01467630174010992,
0.15965452790260315,
0.02063094452023506,
0.0777256190776825,
0.1480122059583664,
-0.030085699632763863,
0.009761088527739048,
0.16861428320407867,
0.16368600726127625,
-0.018452445045113564,
0.0991965... | |
null | null | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln25")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln25")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tr... | {} | text-generation | BigSalmon/InformalToFormalLincoln25 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
-0.010755456984043121,
0.024771401658654213,
-0.005105555523186922,
0.01467630174010992,
0.15965452790260315,
0.02063094452023506,
0.0777256190776825,
0.1480122059583664,
-0.030085699632763863,
0.009761088527739048,
0.16861428320407867,
0.16368600726127625,
-0.018452445045113564,
0.0991965... | |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincolnDistilledGPT2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/InformalToFormalLincolnDistilledGPT2")
```
```
https://huggingface.co/spaces/BigSalmo... | {} | text-generation | BigSalmon/InformalToFormalLincolnDistilledGPT2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln10")
```
```
How To Make Prompt:
Original: freedom of the press is a check against political corruption.
Edited: funda... | {} | text-generation | BigSalmon/MrLincoln10 | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.04233042523264885,
0.04388534650206566,
-0.006762828212231398,
0.027294181287288666,
0.16246283054351807,
0.025923671200871468,
0.13065600395202637,
0.1486392766237259,
0.02448316290974617,
0.009440205059945583,
0.14974473416805267,
0.21966180205345154,
0.00629062857478857,
0.0219342429... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln11")
```
```
How To Make Prompt:
Original: freedom of the press is a check against political corruption.
Edited: funda... | {} | text-generation | BigSalmon/MrLincoln11 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln12")
```
```
https://huggingface.co/spaces/BigSalmon/InformalToFormal
```
```
How To Make Prompt:
informal english: i... | {} | text-generation | BigSalmon/MrLincoln12 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
-0.010755456984043121,
0.024771401658654213,
-0.005105555523186922,
0.01467630174010992,
0.15965452790260315,
0.02063094452023506,
0.0777256190776825,
0.1480122059583664,
-0.030085699632763863,
0.009761088527739048,
0.16861428320407867,
0.16368600726127625,
-0.018452445045113564,
0.0991965... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MrLincoln125MNeo")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln125MNeo")
```
```
https://huggingface.co/spaces/BigSalmon/InformalToFormal
```
```
How To Make ... | {} | text-generation | BigSalmon/MrLincoln125MNeo | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
-0.05047798156738281,
0.03341776132583618,
-0.005883797071874142,
0.020632578060030937,
0.15725530683994293,
0.026660505682229996,
0.10106129199266434,
0.1469801962375641,
0.0042152865789830685,
0.004373022820800543,
0.13822296261787415,
0.210294708609581,
-0.00970395002514124,
0.082323476... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln13")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2_Most_Probable (The model for this space changes over time)
`... | {} | text-generation | BigSalmon/MrLincoln13 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln5")
```
```
https://huggingface.co/spaces/BigSalmon/GPT2 (The model for this space changes over time)
```
```
https:/... | {} | text-generation | BigSalmon/MrLincoln5 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln6")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style ... | {} | text-generation | BigSalmon/MrLincoln6 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Informal to Formal:
```
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelWithLMHead.from_pretrained("BigSalmon/MrLincoln7")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style ... | {} | text-generation | BigSalmon/MrLincoln8 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Informal to Formal:
' | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
0.02414041943848133,
-0.0068230400793254375,
0.010564634576439857,
0.18164798617362976,
0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Example Prompt:
```
informal english: things are better when they are open source, because they are constantly being updated to enhance experience.
Translated into the Style of Abraham Lincoln: in the open-source paradigm, code is ( ceaselessly / perpetually ) being ( reengineered / revamped / polished ), thereby ( adv... | {} | fill-mask | BigSalmon/MrLincolnBerta | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| Example Prompt:
Demo: URL | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
41
] | [
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] | [
-0.017843060195446014,
0.00810796394944191,
-0.00701136002317071,
0.020701615139842033,
0.09613362699747086,
0.02367710880935192,
0.08003920316696167,
0.09647376090288162,
0.04193003475666046,
0.03933814913034439,
0.16214455664157867,
0.1510128676891327,
-0.051603857427835464,
0.1675062775... |
null | null | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/NEO125InformalToFormalLincoln")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/NEO125InformalToFormalLincoln")
```
```
How To Make Prompt:
informal english: i am very ready to do that just ... | {} | text-generation | BigSalmon/NEO125InformalToFormalLincoln | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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-0.02130643092095852,
0.14617420732975006,
0.1955074667930603,
-0.02011914923787117,
0.1474472... | |
null | null | transformers | This can be used to paraphrase. I recommend using the code I have attached below. You can generate it without using LogProbs, but you are likely to be best served by manually examining the most likely outputs.
If this interests you, check out https://huggingface.co/BigSalmon/MrLincoln12 or my other MrLincoln repos.
`... | {} | text-generation | BigSalmon/ParaphraseParentheses | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This can be used to paraphrase. I recommend using the code I have attached below. You can generate it without using LogProbs, but you are likely to be best served by manually examining the most likely outputs.
If this interests you, check out URL or my other MrLincoln repos.
Example Prompt:
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.04233042523264885,
0.04388534650206566,
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0.027294181287288666,
0.16246283054351807,
0.025923671200871468,
0.13065600395202637,
0.1486392766237259,
0.02448316290974617,
0.009440205059945583,
0.14974473416805267,
0.21966180205345154,
0.00629062857478857,
0.0219342429... |
null | null | transformers | This can be used to paraphrase. I recommend using the code I have attached below. You can generate it without using LogProbs, but you are likely to be best served by manually examining the most likely outputs.
If this interests you, check out https://huggingface.co/BigSalmon/MrLincoln12 or my other MrLincoln repos.
`... | {} | text-generation | BigSalmon/ParaphraseParentheses2.0 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This can be used to paraphrase. I recommend using the code I have attached below. You can generate it without using LogProbs, but you are likely to be best served by manually examining the most likely outputs.
If this interests you, check out URL or my other MrLincoln repos.
Example Prompt:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
-0.027653997763991356,
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0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
-0.0068973456509411335,
-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers | Converting Points to Paragraphs
Example Prompts:
```
###
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, ho... | {} | text-generation | BigSalmon/Points | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Converting Points to Paragraphs
Example Prompts:
| [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
55
] | [
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
-0.03178231045603752,
0.04669049009680748,
-0.005260638426989317,
0.022555796429514885,
0.14155633747577667,
0.01801518350839615,
0.11915082484483719,
0.15674573183059692,
0.00008523868018528447,
0.03543689474463463,
0.1649906188249588,
0.17650218307971954,
-0.004087650682777166,
0.0251054... |
null | null | transformers | Converting Points or Headlines to Paragraphs
Example Prompts:
```
###
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be... | {} | text-generation | BigSalmon/Points2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Converting Points or Headlines to Paragraphs
Example Prompts:
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
-0.010755456984043121,
0.024771401658654213,
-0.005105555523186922,
0.01467630174010992,
0.15965452790260315,
0.02063094452023506,
0.0777256190776825,
0.1480122059583664,
-0.030085699632763863,
0.009761088527739048,
0.16861428320407867,
0.16368600726127625,
-0.018452445045113564,
0.0991965... |
null | null | transformers | - All credit goes to https://huggingface.co/philippelaban/keep_it_simple.
- This is a copy of their repository for future training purposes.
- It is supposed to simplify text.
- Their model card gives instructions on how to use it. | {} | text-generation | BigSalmon/SimplifyText | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| - All credit goes to URL
- This is a copy of their repository for future training purposes.
- It is supposed to simplify text.
- Their model card gives instructions on how to use it. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
47
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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0.033704131841659546,
0.08821956068277359,
0.13570955395698547,
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-0.013526750728487968,
0.1547490805387497,
0.20799952745437622,
-0.0026462990790605545,
0.0... |
null | null | transformers |
# Megumin model | {"tags": ["conversational"]} | text-generation | BigTooth/DialoGPT-Megumin | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Megumin model | [
"# Megumin model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Megumin model"
] | [
51,
5
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Megumin model"
] | [
-0.02518005110323429,
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-0.030307041481137276,
0.14001427590847015,
0.21322719752788544,
0.0020618438720703125,
0.1... |
null | null | transformers |
# Tohru DialoGPT model | {"tags": ["conversational"]} | text-generation | BigTooth/DialoGPT-small-tohru | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Tohru DialoGPT model | [
"# Tohru DialoGPT model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Tohru DialoGPT model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Tohru DialoGPT model"
] | [
-0.01709621399641037,
0.03250964730978012,
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0.01048628892749548,
0.1560472697019577,
0.02115590125322342,
0.16008637845516205,
0.1615598499774933,
-0.025013191625475883,
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0.09047058969736099,
0.13441920280456543,
0.028767025098204613,
0.1098692938... |
null | null | transformers |
# Megumin-v0.2 model | {"tags": ["conversational"]} | text-generation | BigTooth/Megumin-v0.2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Megumin-v0.2 model | [
"# Megumin-v0.2 model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Megumin-v0.2 model"
] | [
51,
8
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Megumin-v0.2 model"
] | [
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-0.029726555570960045,
0.14232946932315826,
0.21244291961193085,
0.011954061686992645,
0.1166... |
null | null | transformers |
#Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | text-generation | BigeS/DialoGPT-small-Rick | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick Sanchez DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
51
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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0.14490588009357452,
0.20411323010921478,
-0.006439372431486845,
0.066... |
null | null | null |
<!-- 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. -->
# jplu-wikiann
This model is a fine-tuned version of [jplu/tf-camembert-base](https://huggingface.co/jplu/tf-camembert-base) on th... | {"language": ["fr"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "jplu-wikiann", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "default"}, "metrics": [{"type": "... | null | BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#fr #dataset-wikiann #model-index #region-us
|
# jplu-wikiann
This model is a fine-tuned version of jplu/tf-camembert-base on the wikiann dataset.
It achieves the following results on the evaluation set:
- precision: 0.8980
- recall: 0.9097
- f1: 0.9038
- accuracy: 0.9464
## Model description
More information needed
## Intended uses & limitations
More infor... | [
"# jplu-wikiann\n\nThis model is a fine-tuned version of jplu/tf-camembert-base on the wikiann dataset.\nIt achieves the following results on the evaluation set:\n- precision: 0.8980\n- recall: 0.9097\n- f1: 0.9038\n- accuracy: 0.9464",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"TAGS\n#fr #dataset-wikiann #model-index #region-us \n",
"# jplu-wikiann\n\nThis model is a fine-tuned version of jplu/tf-camembert-base on the wikiann dataset.\nIt achieves the following results on the evaluation set:\n- precision: 0.8980\n- recall: 0.9097\n- f1: 0.9038\n- accuracy: 0.9464",
"## Model descript... | [
18,
74,
6,
12,
8,
3,
83,
35
] | [
"passage: TAGS\n#fr #dataset-wikiann #model-index #region-us \n# jplu-wikiann\n\nThis model is a fine-tuned version of jplu/tf-camembert-base on the wikiann dataset.\nIt achieves the following results on the evaluation set:\n- precision: 0.8980\n- recall: 0.9097\n- f1: 0.9038\n- accuracy: 0.9464## Model description... | [
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0.07327631860971451,
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0.16406723856925... |
null | null | transformers |
# Neku from Twewy | {"tags": ["conversational"]} | text-generation | Bimal/my_bot_model | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Neku from Twewy | [
"# Neku from Twewy"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Neku from Twewy"
] | [
51,
7
] | [
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Neku from Twewy"
] | [
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0.11704378575086594,
0.12853974103927612,
0.02311735413968563,
0.075661584... |
null | null | transformers | ### en_ti_translate
* source languages: en
* target languages: ti
* model: hugging face transformer seq2seq
* base model : opus-mt-en-ti
* pre-processing: normalization + SentencePiece
### documentation
https://tigrinyanlp.github.io/
| {"tags": ["translation"]} | translation | Biniam/en_ti_translate | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #marian #text2text-generation #translation #autotrain_compatible #endpoints_compatible #region-us
| ### en_ti_translate
* source languages: en
* target languages: ti
* model: hugging face transformer seq2seq
* base model : opus-mt-en-ti
* pre-processing: normalization + SentencePiece
### documentation
URL
| [
"### en_ti_translate\n* source languages: en\n* target languages: ti\n* model: hugging face transformer seq2seq\n* base model : opus-mt-en-ti\n* pre-processing: normalization + SentencePiece",
"### documentation\nURL"
] | [
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"### en_ti_translate\n* source languages: en\n* target languages: ti\n* model: hugging face transformer seq2seq\n* base model : opus-mt-en-ti\n* pre-processing: normalization + Sent... | [
42,
58,
4
] | [
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #translation #autotrain_compatible #endpoints_compatible #region-us \n### en_ti_translate\n* source languages: en\n* target languages: ti\n* model: hugging face transformer seq2seq\n* base model : opus-mt-en-ti\n* pre-processing: normalization + S... | [
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null | null | transformers |
# My Awesome Model | {"tags": ["conversational"]} | text-generation | BinksSachary/DialoGPT-small-shaxx | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
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null | null | transformers |
# My Awesome Model | {"tags": ["conversational"]} | text-generation | BinksSachary/ShaxxBot | [
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"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# My Awesome Model
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
# Let's chat for 4 lines
for step in range(4):
# encode the new ... | {"tags": ["conversational"]} | text-generation | BinksSachary/ShaxxBot2 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
# Let's chat for 4 lines
for step in range(4):
# encode the new ... | [
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null | null | 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. -->
# hackMIT-finetuned-sst2
This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "hackMIT-finetuned-sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metric": {"name": "Accuracy", "type": ... | text-classification | Blaine-Mason/hackMIT-finetuned-sst2 | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #autotrain_compatible #endpoints_compatible #region-us
| hackMIT-finetuned-sst2
======================
This model is a fine-tuned version of Blaine-Mason/hackMIT-finetuned-sst2 on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1086
* Accuracy: 0.8028
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.033238621168611e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 30\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1"... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.033238621168611e-06\n* train... | [
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null | null | transformers |
# A new medium model based on the character Makise Kurisu from Steins;Gate.
# Still has some issues that were present in the previous model, for example, mixing lines from other characters.
# If you have any questions, feel free to ask me on discord: BlightZz#1169 | {"tags": ["conversational"]} | text-generation | BlightZz/DialoGPT-medium-Kurisu | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# A new medium model based on the character Makise Kurisu from Steins;Gate.
# Still has some issues that were present in the previous model, for example, mixing lines from other characters.
# If you have any questions, feel free to ask me on discord: BlightZz#1169 | [
"# A new medium model based on the character Makise Kurisu from Steins;Gate.",
"# Still has some issues that were present in the previous model, for example, mixing lines from other characters.",
"# If you have any questions, feel free to ask me on discord: BlightZz#1169"
] | [
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null | null | transformers |
# A small model based on the character Makise Kurisu from Steins;Gate. This was made as a test.
# A new medium model was made using her lines, I also added some fixes. It can be found here:
# https://huggingface.co/BlightZz/DialoGPT-medium-Kurisu | {"tags": ["conversational"]} | text-generation | BlightZz/MakiseKurisu | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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# A small model based on the character Makise Kurisu from Steins;Gate. This was made as a test.
# A new medium model was made using her lines, I also added some fixes. It can be found here:
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null | null | transformers |
Dataset Link - https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection | {"language": ["English"], "tags": ["Text", "Sequence-Classification", "Sarcasm", "DistilBert"], "datasets": ["Kaggle Dataset"], "metrics": ["precision", "recall", "f1"]} | text-classification | BlindMan820/Sarcastic-News-Headlines | [
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"text-classification",
"Text",
"Sequence-Classification",
"Sarcasm",
"DistilBert",
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] | 2022-03-02T23:29:04+00:00 | [] | [
"English"
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|
Dataset Link - URL | [] | [
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null | null | transformers |
# Moragna DialoGPT Model | {"tags": ["conversational"]} | text-generation | BlueGamerBeast/DialoGPT-small-Morgana | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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null | null | transformers | # Korean bert base model for DST
- This is ConversationBert for dsksd/bert-ko-small-minimal(base-module) + 5 datasets
- Use dsksd/bert-ko-small-minimal tokenizer
- 5 datasets
- tweeter_dialogue : xlsx
- speech : trn
- office_dialogue : json
- KETI_dialogue : txt
- WOS_dataset : json
```python
tokenizer = ... | {} | null | BonjinKim/dst_kor_bert | [
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us
| # Korean bert base model for DST
- This is ConversationBert for dsksd/bert-ko-small-minimal(base-module) + 5 datasets
- Use dsksd/bert-ko-small-minimal tokenizer
- 5 datasets
- tweeter_dialogue : xlsx
- speech : trn
- office_dialogue : json
- KETI_dialogue : txt
- WOS_dataset : json
| [
"# Korean bert base model for DST\n\n- This is ConversationBert for dsksd/bert-ko-small-minimal(base-module) + 5 datasets\n- Use dsksd/bert-ko-small-minimal tokenizer\n- 5 datasets\n - tweeter_dialogue : xlsx\n - speech : trn\n - office_dialogue : json\n - KETI_dialogue : txt\n - WOS_dataset : json"
] | [
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null | null | transformers |
# DialoGPT Model for Penny | {"tags": ["conversational"]} | text-generation | BotterHax/DialoGPT-small-harrypotter | [
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"conversational",
"autotrain_compatible",
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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null | null | transformers |
# British Library Books Genre Detector
**Note** this model card is a work in progress.
## Model description
This fine-tuned [`distilbert-base-cased`](https://huggingface.co/distilbert-base-cased) model is trained to predict whether a book from the [British Library's](https://www.bl.uk/) [Digitised printed books (... | {"language": ["multilingual", "en", "ru", "fr", "es", "de", "nl", "it", "sv", "da", "hu", "pl", "la", "el", "cs", "pt", "fi", "sr", "bg", "is", "ga", "he", "nn", "lt", "sl", "kw", "ro", "sk", "sco", "sa"], "license": "mit", "tags": ["genre", "books", "library", "historic", "glam ", "lam"], "datasets": ["TheBritishLibra... | text-classification | TheBritishLibrary/bl-books-genre | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"multilingual",
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"pl",
"la",
"el",
"cs",
"pt",
"fi",
"sr",
"bg"... | 2022-03-02T23:29:04+00:00 | [] | [
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] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #genre #books #library #historic #glam #lam #multilingual #en #ru #fr #es #de #nl #it #sv #da #hu #pl #la #el #cs #pt #fi #sr #bg #is #ga #he #nn #lt #sl #kw #ro #sk #sco #sa #dataset-TheBritishLibrary/blbooksgenre #license-mit #autotrain_compat... | British Library Books Genre Detector
====================================
Note this model card is a work in progress.
Model description
-----------------
This fine-tuned 'distilbert-base-cased' model is trained to predict whether a book from the British Library's Digitised printed books (18th-19th century) book c... | [
"### Title format\n\n\nThe model's training data (discussed more below) primarily consists of 19th Century book titles that have been catalogued according to British Library cataloguing practices. Since the approaches taken to cataloguing will vary across institutions running the model on titles from a different ca... | [
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null | null | transformers | #Harry Potter DialoGPT Model | {"tags": "conversational"} | text-generation | Broadus20/DialoGPT-small-joshua | [
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null | null | transformers |
#DialoGPT-kungfupanda | {"tags": ["conversational"]} | text-generation | BrunoNogueira/DialoGPT-kungfupanda | [
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] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
#DialoGPT-kungfupanda | [] | [
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null | null | transformers |
# Morty DialoGPT Model | {"tags": ["conversational"]} | text-generation | Brykee/DialoGPT-medium-Morty | [
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"conversational",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
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null | null | transformers | # Harry Potter speech | {"tags": ["conversational"]} | text-generation | Bubb-les/DisloGPT-medium-HarryPotter | [
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null | null | 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. -->
# TRUMP
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model description
Mor... | {"license": "mit", "tags": ["generated_from_trainer"], "model_index": [{"name": "TRUMP", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | text-generation | BumBelDumBel/TRUMP | [
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"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# TRUMP
This model is a fine-tuned version of gpt2 on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparamete... | [
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null | null | 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. -->
# ZORK-AI-TEST
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unkown dataset.
## Model descripti... | {"license": "mit", "tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK-AI-TEST", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | text-generation | BumBelDumBel/ZORK-AI-TEST | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# ZORK-AI-TEST
This model is a fine-tuned version of gpt2 on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperp... | [
"# ZORK-AI-TEST\n\nThis model is a fine-tuned version of gpt2 on an unkown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperpa... | [
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null | null | 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. -->
# ZORK_AI_SCIFI
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unkown dataset.
## ... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "ZORK_AI_SCIFI", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | text-generation | BumBelDumBel/ZORK_AI_SCIFI | [
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"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# ZORK_AI_SCIFI
This model is a fine-tuned version of gpt2-medium on an unkown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The followin... | [
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"## Model description\n\nMore information needed",
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"## Training and evaluation data\n\nMore information needed",
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null | null | 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | token-classification | Buntan/bert-finetuned-ner | [
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"bert",
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"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0612
* Precision: 0.9329
* Recall: 0.9517
* F1: 0.9422
* Accuracy: 0.9863
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... | [
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null | null | transformers | # CAMeLBERT-CA NER Model
## Model description
**CAMeLBERT-CA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [ANERcorp](https://camel.abudhab... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-CA NER Model
## Model description
CAMeLBERT-CA NER Model is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Classical Arabic (CA) model.
For the fine-tuning, we used the ANERcorp dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *... | [
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null | null | transformers | # CAMeLBERT-CA Poetry Classification Model
## Model description
**CAMeLBERT-CA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [APCD... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u062e\u064a\u0644 \u0648\u0627\u0644\u0644\u064a\u0644 \u0648\u0627\u0644\u0628\u064a\u062f\u0627\u0621 \u062a\u0639\u0631\u0641\u0646\u064a [SEP] \u0648\u0627\u0644\u0633\u064a\u0641 \u0648\u0627\u0644\u0631\u0645\u062d \u0648\u0627\u0644\... | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
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"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"license:apache-2.0",
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"1905.05700",
"2103.06678"
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] | TAGS
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| # CAMeLBERT-CA Poetry Classification Model
## Model description
CAMeLBERT-CA Poetry Classification Model is a poetry classification model that was built by fine-tuning the CAMeLBERT Classical Arabic (CA) model.
For the fine-tuning, we used the APCD dataset.
Our fine-tuning procedure and the hyperparameters we used can ... | [
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null | null | transformers | # CAMeLBERT-CA POS-EGY Model
## Model description
**CAMeLBERT-CA POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and ... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy | [
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#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CAMeLBERT-CA POS-EGY Model
## Model description
CAMeLBERT-CA POS-EGY Model is a Egyptian Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-CA model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay o... | [
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null | null | transformers | # CAMeLBERT-CA POS-GLF Model
## Model description
**CAMeLBERT-CA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-g... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0634\u0644\u0648\u0646\u0643 \u061f \u0634\u062e\u0628\u0627\u0631\u0643 \u061f"}]} | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf | [
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"region:us"
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#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-CA POS-GLF Model
## Model description
CAMeLBERT-CA POS-GLF Model is a Gulf Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-CA model.
For the fine-tuning, we used the Gumar dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Var... | [
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null | null | transformers | # CAMeLBERT-CA POS-MSA Model
## Model description
**CAMeLBERT-CA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-CA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
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"token-classification",
"ar",
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"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
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] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-CA POS-MSA Model
## Model description
CAMeLBERT-CA POS-MSA Model is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the CAMeLBERT-CA model.
For the fine-tuning, we used the PATB dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The ... | [
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null | null | transformers | # CAMeLBERT-CA SA Model
## Model description
**CAMeLBERT-CA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Classical Arabic (CA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/) model.
For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0623\u0646\u0627 \u0628\u062e\u064a\u0631"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment | [
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"license:apache-2.0",
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"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
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] | TAGS
#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CAMeLBERT-CA SA Model
## Model description
CAMeLBERT-CA SA Model is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Classical Arabic (CA) model.
For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets.
Our fine-tuning procedure and the hyperparameters we used can be found in our... | [
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null | null | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | fill-mask | CAMeL-Lab/bert-base-arabic-camelbert-ca | [
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| CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
==================================================================
Model description
-----------------
CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n*Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually.\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nan... | [
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null | null | transformers | # CAMeLBERT-DA NER Model
## Model description
**CAMeLBERT-DA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [ANERcorp](https://camel.abudhab... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-da-ner | [
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"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
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] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-DA NER Model
## Model description
CAMeLBERT-DA NER Model is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model.
For the fine-tuning, we used the ANERcorp dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *... | [
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null | null | transformers | # CAMeLBERT-DA Poetry Classification Model
## Model description
**CAMeLBERT-DA Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [APCD... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u062e\u064a\u0644 \u0648\u0627\u0644\u0644\u064a\u0644 \u0648\u0627\u0644\u0628\u064a\u062f\u0627\u0621 \u062a\u0639\u0631\u0641\u0646\u064a [SEP] \u0648\u0627\u0644\u0633\u064a\u0641 \u0648\u0627\u0644\u0631\u0645\u062d \u0648\u0627\u0644\... | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-da-poetry | [
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"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1905.05700",
"2103.06678"
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#transformers #pytorch #tf #bert #text-classification #ar #arxiv-1905.05700 #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-DA Poetry Classification Model
## Model description
CAMeLBERT-DA Poetry Classification Model is a poetry classification model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model.
For the fine-tuning, we used the APCD dataset.
Our fine-tuning procedure and the hyperparameters we used can ... | [
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null | null | transformers | # CAMeLBERT-DA POS-EGY Model
## Model description
**CAMeLBERT-DA POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and ... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy | [
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| # CAMeLBERT-DA POS-EGY Model
## Model description
CAMeLBERT-DA POS-EGY Model is a Egyptian Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-DA model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay o... | [
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null | null | transformers | # CAMeLBERT-DA POS-GLF Model
## Model description
**CAMeLBERT-DA POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotated-g... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0634\u0644\u0648\u0646\u0643 \u061f \u0634\u062e\u0628\u0627\u0631\u0643 \u061f"}]} | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf | [
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"token-classification",
"ar",
"arxiv:2103.06678",
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"autotrain_compatible",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-DA POS-GLF Model
## Model description
CAMeLBERT-DA POS-GLF Model is a Gulf Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-DA model.
For the fine-tuning, we used the Gumar dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Var... | [
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null | null | transformers | # CAMeLBERT-DA POS-MSA Model
## Model description
**CAMeLBERT-DA POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-DA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi/pdf... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa | [
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"token-classification",
"ar",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
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#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-DA POS-MSA Model
## Model description
CAMeLBERT-DA POS-MSA Model is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the CAMeLBERT-DA model.
For the fine-tuning, we used the PATB dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The ... | [
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null | null | transformers | # CAMeLBERT-DA SA Model
## Model description
**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Dialectal Arabic (DA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-da/) model.
For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0623\u0646\u0627 \u0628\u062e\u064a\u0631"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment | [
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"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
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"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
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] | TAGS
#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CAMeLBERT-DA SA Model
## Model description
CAMeLBERT-DA SA Model is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model.
For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets.
Our fine-tuning procedure and the hyperparameters we used can be found in our... | [
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null | null | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | fill-mask | CAMeL-Lab/bert-base-arabic-camelbert-da | [
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| CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
==================================================================
Model description
-----------------
CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n*Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually.\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nan... | [
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null | null | transformers | # CAMeLBERT-Mix DID Madar Corpus26 Model
## Model description
**CAMeLBERT-Mix DID Madar Corpus26 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [MADAR Corpus 26](h... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
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"tf",
"bert",
"text-classification",
"ar",
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"region:us"
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#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix DID Madar Corpus26 Model
## Model description
CAMeLBERT-Mix DID Madar Corpus26 Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT-Mix model.
For the fine-tuning, we used the MADAR Corpus 26 dataset, which includes 26 labels.
Our fine-tuning procedure and the hyperp... | [
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null | null | transformers | # CAMeLBERT-Mix DID MADAR Corpus6 Model
## Model description
**CAMeLBERT-Mix DID MADAR Corpus6 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [MADAR Corpus 6](http... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
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"license:apache-2.0",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
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#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix DID MADAR Corpus6 Model
## Model description
CAMeLBERT-Mix DID MADAR Corpus6 Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT-Mix model.
For the fine-tuning, we used the MADAR Corpus 6 dataset, which includes 6 labels.
Our fine-tuning procedure and the hyperparam... | [
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null | null | transformers | # CAMeLBERT-Mix DID NADI Model
## Model description
**CAMeLBERT-Mix DID NADI Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [NADI Coountry-level](https://sites.goo... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix DID NADI Model
## Model description
CAMeLBERT-Mix DID NADI Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT-Mix model.
For the fine-tuning, we used the NADI Coountry-level dataset, which includes 21 labels.
Our fine-tuning procedure and the hyperparameters we use... | [
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null | null | transformers | # CAMeLBERT-Mix NER Model
## Model description
**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [ANERcorp](https://camel.abudhabi.nyu.edu/anerc... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"transformers",
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"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
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] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # CAMeLBERT-Mix NER Model
## Model description
CAMeLBERT-Mix NER Model is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Mix model.
For the fine-tuning, we used the ANERcorp dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay o... | [
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null | null | transformers | # CAMeLBERT-Mix Poetry Classification Model
## Model description
**CAMeLBERT-Mix Poetry Classification Model** is a poetry classification model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [APCD](https://arxiv... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u062e\u064a\u0644 \u0648\u0627\u0644\u0644\u064a\u0644 \u0648\u0627\u0644\u0628\u064a\u062f\u0627\u0621 \u062a\u0639\u0631\u0641\u0646\u064a [SEP] \u0648\u0627\u0644\u0633\u064a\u0641 \u0648\u0627\u0644\u0631\u0645\u062d \u0648\u0627\u0644\... | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"transformers",
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"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"1905.05700",
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#transformers #pytorch #tf #bert #text-classification #ar #arxiv-1905.05700 #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix Poetry Classification Model
## Model description
CAMeLBERT-Mix Poetry Classification Model is a poetry classification model that was built by fine-tuning the CAMeLBERT Mix model.
For the fine-tuning, we used the APCD dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our ... | [
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null | null | transformers | # CAMeLBERT-Mix POS-EGY Model
## Model description
**CAMeLBERT-Mix POS-EGY Model** is a Egyptian Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure ... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
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"pytorch",
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"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
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"ar"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix POS-EGY Model
## Model description
CAMeLBERT-Mix POS-EGY Model is a Egyptian Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-Mix model.
For the fine-tuning, we used the ARZTB dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interpla... | [
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null | null | transformers | # CAMeLBERT-Mix POS-GLF Model
## Model description
**CAMeLBERT-Mix POS-GLF Model** is a Gulf Arabic POS tagging model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [Gumar](https://camel.abudhabi.nyu.edu/annotat... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0634\u0644\u0648\u0646\u0643 \u061f \u0634\u062e\u0628\u0627\u0631\u0643 \u061f"}]} | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix POS-GLF Model
## Model description
CAMeLBERT-Mix POS-GLF Model is a Gulf Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-Mix model.
For the fine-tuning, we used the Gumar dataset .
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of... | [
"# CAMeLBERT-Mix POS-GLF Model",
"## Model description\nCAMeLBERT-Mix POS-GLF Model is a Gulf Arabic POS tagging model that was built by fine-tuning the CAMeLBERT-Mix model.\nFor the fine-tuning, we used the Gumar dataset .\nOur fine-tuning procedure and the hyperparameters we used can be found in our paper *\"Th... | [
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null | null | transformers | # CAMeLBERT-Mix POS-MSA Model
## Model description
**CAMeLBERT-Mix POS-MSA Model** is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the [CAMeLBERT-Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [PATB](https://dl.acm.org/doi... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0625\u0645\u0627\u0631\u0629 \u0623\u0628\u0648\u0638\u0628\u064a \u0647\u064a \u0625\u062d\u062f\u0649 \u0625\u0645\u0627\u0631\u0627\u062a \u062f\u0648\u0644\u0629 \u0627\u0644\u0625\u0645\u0627\u0631\u0627\u062a \u0627\u0644\u0639\u0631\u0628\u064a... | token-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa | [
"transformers",
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #token-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-Mix POS-MSA Model
## Model description
CAMeLBERT-Mix POS-MSA Model is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the CAMeLBERT-Mix model.
For the fine-tuning, we used the PATB dataset.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"T... | [
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"## Model description\nCAMeLBERT-Mix POS-MSA Model is a Modern Standard Arabic (MSA) POS tagging model that was built by fine-tuning the CAMeLBERT-Mix model.\nFor the fine-tuning, we used the PATB dataset.\nOur fine-tuning procedure and the hyperparameters we used can be found in ... | [
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null | null | transformers | # CAMeLBERT Mix SA Model
## Model description
**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the [CAMeLBERT Mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix/) model.
For the fine-tuning, we used the [ASTD](https://aclanthology.org/D15-1299.pdf), [ArSAS](h... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0623\u0646\u0627 \u0628\u062e\u064a\u0631"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT Mix SA Model
## Model description
CAMeLBERT Mix SA Model is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Mix model.
For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets.
Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Int... | [
"# CAMeLBERT Mix SA Model",
"## Model description\nCAMeLBERT Mix SA Model is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Mix model.\nFor the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets.\nOur fine-tuning procedure and the hyperparameters we used can be found in our pa... | [
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null | null | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "tags": ["Arabic", "Dialect", "Egyptian", "Gulf", "Levantine", "Classical Arabic", "MSA", "Modern Standard Arabic"], "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | fill-mask | CAMeL-Lab/bert-base-arabic-camelbert-mix | [
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"Modern Standard Arabic",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
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"region:us"
] | 2022-03-02T23:29:04+00:00 | [
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| CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
==================================================================
Model description
-----------------
CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n*Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually.\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nan... | [
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null | null | transformers | # CAMeLBERT-MSA DID MADAR Twitter-5 Model
## Model description
**CAMeLBERT-MSA DID MADAR Twitter-5 Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT-MSA](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [MADAR Twitter-5]... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-MSA DID MADAR Twitter-5 Model
## Model description
CAMeLBERT-MSA DID MADAR Twitter-5 Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT-MSA model.
For the fine-tuning, we used the MADAR Twitter-5 dataset, which includes 21 labels.
Our fine-tuning procedure and the hype... | [
"# CAMeLBERT-MSA DID MADAR Twitter-5 Model",
"## Model description\nCAMeLBERT-MSA DID MADAR Twitter-5 Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT-MSA model.\nFor the fine-tuning, we used the MADAR Twitter-5 dataset, which includes 21 labels.\nOur fine-tuning procedure... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CAMeLBERT-MSA DID MADAR Twitter-5 Model",
"## Model description\nCAMeLBERT-MSA DID MADAR Twitter-5 Model is a dialect identification (DID) model t... | [
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null | null | transformers | # CAMeLBERT-MSA DID NADI Model
## Model description
**CAMeLBERT-MSA DID NADI Model** is a dialect identification (DID) model that was built by fine-tuning the [CAMeLBERT Modern Standard Arabic (MSA)](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa/) model.
For the fine-tuning, we used the [NADI Coountry... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0639\u0627\u0645\u0644 \u0627\u064a\u0647 \u061f"}]} | text-classification | CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # CAMeLBERT-MSA DID NADI Model
## Model description
CAMeLBERT-MSA DID NADI Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model.
For the fine-tuning, we used the NADI Coountry-level dataset, which includes 21 labels.
Our fine-tuning procedure and t... | [
"# CAMeLBERT-MSA DID NADI Model",
"## Model description\nCAMeLBERT-MSA DID NADI Model is a dialect identification (DID) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model.\nFor the fine-tuning, we used the NADI Coountry-level dataset, which includes 21 labels.\nOur fine-tuning pr... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# CAMeLBERT-MSA DID NADI Model",
"## Model description\nCAMeLBERT-MSA DID NADI Model is a dialect identification (DID) model that was built by fine-... | [
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"passage: TAGS\n#transformers #pytorch #tf #bert #text-classification #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# CAMeLBERT-MSA DID NADI Model## Model description\nCAMeLBERT-MSA DID NADI Model is a dialect identification (DID) model that was built by fine-tun... | [
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null | null | transformers |
# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
## Model description
**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (C... | {"language": ["ar"], "license": "apache-2.0", "widget": [{"text": "\u0627\u0644\u0647\u062f\u0641 \u0645\u0646 \u0627\u0644\u062d\u064a\u0627\u0629 \u0647\u0648 [MASK] ."}]} | fill-mask | CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
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"ar",
"arxiv:2103.06678",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04+00:00 | [
"2103.06678"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
==================================================================
Model description
-----------------
CAMeLBERT is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
We release pre-trained language models for... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n*Note*: to download our models, you would need 'transformers>=3.5.0'. Otherwise, you could download the models manually.\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nan... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n*Note*: to download our models, you would need 'tra... | [
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"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #ar #arxiv-2103.06678 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n*Note*: to download our models, you would need '... | [
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