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text-generation | 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. -->
# clm-total
This model is a fine-tuned version of [ckiplab/gpt2-base-chinese](https://huggingface.co/ckiplab/gpt2-base-chinese) on... | {"language": ["zh"], "license": "gpl-3.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "clm-total", "results": []}]} | Littlemilk/autobiography-generator | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"zh",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# clm-total
This model is a fine-tuned version of ckiplab/gpt2-base-chinese on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More in... | [
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"## Intended uses & limitations\n\nMore information needed",
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text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | Lizardon/Peterbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peter from Your Boyfriend Game.
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fill-mask | transformers |
# QuBERTa
QuBERTa es un modelo de lenguaje basado en RoBERTa para el quechua. Nuestro modelo de lenguaje fue pre-entrenado con 5M de tokens del quechua sureño (Collao y Chanka).
El modelo utiliza un tokenizador Byte-level BPE con un vocabulario de 52000 tokens de subpalabras.
## Usabilidad
Una vez descargado los ... | {"language": ["qu"], "tags": ["Llamacha"]} | Llamacha/QuBERTa | null | [
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"pytorch",
"roberta",
"fill-mask",
"Llamacha",
"qu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"qu"
] | TAGS
#transformers #pytorch #roberta #fill-mask #Llamacha #qu #autotrain_compatible #endpoints_compatible #region-us
|
# QuBERTa
QuBERTa es un modelo de lenguaje basado en RoBERTa para el quechua. Nuestro modelo de lenguaje fue pre-entrenado con 5M de tokens del quechua sureño (Collao y Chanka).
El modelo utiliza un tokenizador Byte-level BPE con un vocabulario de 52000 tokens de subpalabras.
## Usabilidad
Una vez descargado los ... | [
"# QuBERTa \n\nQuBERTa es un modelo de lenguaje basado en RoBERTa para el quechua. Nuestro modelo de lenguaje fue pre-entrenado con 5M de tokens del quechua sureño (Collao y Chanka).\n\nEl modelo utiliza un tokenizador Byte-level BPE con un vocabulario de 52000 tokens de subpalabras.",
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null | null | This model is for anyone using using Flux.jl and looking for a test model to make sue of the Hugging Face hub. You can see the source code to generate this model below:
```Julia
julia> using Flux
julia> model = Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax)
Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NN... | {} | LoganKilpatrick/BasicFluxjlModel | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| This model is for anyone using using URL and looking for a test model to make sue of the Hugging Face hub. You can see the source code to generate this model below:
you can then load the model in Julia as follows:
See here: URL for more details! | [] | [
"TAGS\n#region-us \n"
] | [
5
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null | null | Aaaa | {} | Lolamarcon/Migo | null | [
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#region-us
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null | null | ## README | {} | Longines/test_repo | null | [
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text-generation | transformers |
# GePpeTto GPT2 Model 🇮🇹
Pretrained GPT2 117M model for Italian.
You can find further details in the paper:
Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim, Marco Guerini "GePpeTto Carves Italian into a Language Model", arXiv preprint. Pdf available at: https://arxiv.org/abs/2004.14253
##... | {"language": "it"} | LorenzoDeMattei/GePpeTto | null | [
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"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"it",
"arxiv:2004.14253",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2004.14253"
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#transformers #pytorch #jax #safetensors #gpt2 #text-generation #it #arxiv-2004.14253 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| GePpeTto GPT2 Model 🇮🇹
======================
Pretrained GPT2 117M model for Italian.
You can find further details in the paper:
Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim, Marco Guerini "GePpeTto Carves Italian into a Language Model", arXiv preprint. Pdf available at: URL
Pretrai... | [] | [
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] |
image-classification | transformers |
# lawn-weeds
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | LorenzoDeMattei/lawn-weeds | null | [
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"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# lawn-weeds
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### clover
!clover
#### dichondra
!dichondra
#### grass
!grass | [
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question-answering | transformers | ## AllenAI's <i>scibert_scivocab_uncased</i> fine-tuned on SQuAD 2.0 evaluated with F1 = 86.85
#### To load the model:
```
from transformers import BertTokenizerFast
from transformers import BertForQuestionAnswering
tokenizer = BertTokenizerFast.from_pretrained("LoudlySoft/scibert_scivocab_uncased_squad")
model = B... | {} | LoudlySoft/scibert_scivocab_uncased_squad | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #endpoints_compatible #region-us
| ## AllenAI's <i>scibert_scivocab_uncased</i> fine-tuned on SQuAD 2.0 evaluated with F1 = 86.85
#### To load the model:
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] |
text-generation | transformers |
# Aqua | {"tags": ["conversational"]} | Lovery/Aqua | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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fill-mask | transformers | ```python
import jieba_fast
from transformers import BertTokenizer
from transformers import BigBirdModel
class JiebaTokenizer(BertTokenizer):
def __init__(
self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
):
super().__init__(*args, **kwargs)
self.pre_tokenizer ... | {"language": ["zh"], "license": ["apache-2.0"]} | Lowin/chinese-bigbird-base-4096 | null | [
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
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|
URL | [] | [
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fill-mask | transformers | ```python
import jieba_fast
from transformers import BertTokenizer
from transformers import BigBirdModel
class JiebaTokenizer(BertTokenizer):
def __init__(
self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
):
super().__init__(*args, **kwargs)
self.pre_tokenizer ... | {"language": ["zh"], "license": ["apache-2.0"]} | Lowin/chinese-bigbird-mini-1024 | null | [
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #big_bird #fill-mask #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
URL | [] | [
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41
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] |
feature-extraction | transformers | ```python
import jieba_fast
from transformers import BertTokenizer
from transformers import BigBirdModel
class JiebaTokenizer(BertTokenizer):
def __init__(
self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
):
super().__init__(*args, **kwargs)
self.pre_tokenizer ... | {"language": ["zh"], "license": ["apache-2.0"]} | Lowin/chinese-bigbird-small-1024 | null | [
"transformers",
"pytorch",
"big_bird",
"feature-extraction",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #big_bird #feature-extraction #zh #license-apache-2.0 #endpoints_compatible #region-us
|
URL
| [] | [
"TAGS\n#transformers #pytorch #big_bird #feature-extraction #zh #license-apache-2.0 #endpoints_compatible #region-us \n"
] | [
36
] | [
"TAGS\n#transformers #pytorch #big_bird #feature-extraction #zh #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
```python
import jieba_fast
from transformers import BertTokenizer
from transformers import BigBirdModel
class JiebaTokenizer(BertTokenizer):
def __init__(
self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
):
super().__init__(*args, **kwargs)
self.pre_tokenize... | {"language": ["zh"], "license": ["apache-2.0"]} | Lowin/chinese-bigbird-tiny-1024 | null | [
"transformers",
"pytorch",
"big_bird",
"feature-extraction",
"zh",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #big_bird #feature-extraction #zh #license-apache-2.0 #endpoints_compatible #region-us
|
URL | [] | [
"TAGS\n#transformers #pytorch #big_bird #feature-extraction #zh #license-apache-2.0 #endpoints_compatible #region-us \n"
] | [
36
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] |
fill-mask | transformers | ```python
from transformers import BertTokenizer
from transformers import BigBirdModel
model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-wwm-base-4096')
tokenizer = BertTokenizer.from_pretrained('Lowin/chinese-bigbird-wwm-base-4096')
```
https://github.com/LowinLi/chinese-bigbird | {"language": ["zh"], "license": ["apache-2.0"]} | Lowin/chinese-bigbird-wwm-base-4096 | null | [
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"pytorch",
"big_bird",
"fill-mask",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #big_bird #fill-mask #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
URL | [] | [
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] |
null | null | First-try | {} | LucasLi/Transformer | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| First-try | [] | [
"TAGS\n#region-us \n"
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text-generation | transformers | # XiaoBot for Discord
[Tutorial](https://youtu.be/UjDpW_SOrlw) followed for this model. | {"tags": ["conversational"]} | Lucdi90/DialoGPT-medium-XiaoBot | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # XiaoBot for Discord
Tutorial followed for this model. | [
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] |
fill-mask | 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-base-portuguese-cased-finetuned-peticoes
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](http... | {"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Com efeito, se tal fosse poss\u00edvel, o Poder [MASK] \u2013 que n\u00e3o disp\u00f5e de fun\u00e7\u00e3o legislativa \u2013 passaria a desempenhar atribui\u00e7\u00e3o que lhe \u00e9 institucionalmente estranha (a de legis... | Luciano/bert-base-portuguese-cased-finetuned-peticoes | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"pt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #pt #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased-finetuned-peticoes
=============================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0878
Model description
-----------------
More informatio... | [
"### 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: 3.0",
"### Traini... | [
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fill-mask | 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-base-portuguese-cased-finetuned-tcu-acordaos
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](... | {"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Com efeito, se tal fosse poss\u00edvel, o Poder [MASK] \u2013 que n\u00e3o disp\u00f5e de fun\u00e7\u00e3o legislativa \u2013 passaria a desempenhar atribui\u00e7\u00e3o que lhe \u00e9 institucionalmente estranha (a de legis... | Luciano/bert-base-portuguese-cased-finetuned-tcu-acordaos | null | [
"transformers",
"pytorch",
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"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"pt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #pt #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-portuguese-cased-finetuned-tcu-acordaos
=================================================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5765
Model description
-----------------
More in... | [
"### 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: 3.0",
"### Traini... | [
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47,
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token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertimbau-base-lener_br
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neu... | {"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lener_br"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bertimbau-base-lener_br", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "lener... | Luciano/bertimbau-base-lener_br | null | [
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"base_model:neuralmind/bert-base-portuguese-cased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
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| bertimbau-base-lener\_br
========================
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the lener\_br dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2298
* Precision: 0.8501
* Recall: 0.9138
* F1: 0.8808
* Accuracy: 0.9693
Model description
---... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #base_model-neuralmind/bert-base-portuguese-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following... | [
75,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #base_model-neuralmind/bert-base-portuguese-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyper... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertimbau-large-lener_br
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/n... | {"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["lener_br"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bertimbau-large-lener_br", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "lene... | Luciano/bertimbau-large-lener_br | null | [
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"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
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| bertimbau-large-lener\_br
=========================
This model is a fine-tuned version of neuralmind/bert-large-portuguese-cased on the lener\_br dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1271
* Precision: 0.8965
* Recall: 0.9198
* F1: 0.9080
* Accuracy: 0.9801
Model description
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #token-classification #generated_from_trainer #pt #dataset-lener_br #base_model-neuralmind/bert-large-portuguese-cased #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe followin... | [
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44
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text-generation | 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. -->
# gpt2-small-portuguese-finetuned-peticoes
This model is a fine-tuned version of [pierreguillou/gpt2-small-portuguese](https://hug... | {"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "base_model": "pierreguillou/gpt2-small-portuguese", "model-index": [{"name": "gpt2-small-portuguese-finetuned-peticoes", "results": []}]} | Luciano/gpt2-small-portuguese-finetuned-peticoes | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
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| gpt2-small-portuguese-finetuned-peticoes
========================================
This model is a fine-tuned version of pierreguillou/gpt2-small-portuguese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.4062
Model description
-----------------
More information needed
I... | [
"### 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: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparam... | [
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text-generation | 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. -->
# gpt2-small-portuguese-finetuned-tcu-acordaos
This model is a fine-tuned version of [pierreguillou/gpt2-small-portuguese](https:/... | {"language": ["pt"], "license": "mit", "tags": ["generated_from_trainer"], "base_model": "pierreguillou/gpt2-small-portuguese", "model-index": [{"name": "gpt2-small-portuguese-finetuned-tcu-acordaos", "results": []}]} | Luciano/gpt2-small-portuguese-finetuned-tcu-acordaos | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"pt"
] | TAGS
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| gpt2-small-portuguese-finetuned-tcu-acordaos
============================================
This model is a fine-tuned version of pierreguillou/gpt2-small-portuguese on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6841
Model description
-----------------
More information ne... | [
"### 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: 3.0",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #pt #base_model-pierreguillou/gpt2-small-portuguese #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparam... | [
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text-generation | transformers |
# Jake Peralta B99 DialoGPT Model | {"tags": ["conversational"]} | LuckyWill/DialoGPT-small-JakeBot | null | [
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"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Spanish
Added custom language model to https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice).
... | {"language": "es", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "es", "hf-asr-leaderboard", "mozilla-foundation/common_voice_6_0", "robust-speech-event", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "mozilla-foundation/common_voice_6_0"], "metrics": ["wer", "cer"], "mod... | LuisG07/wav2vec2-large-xlsr-53-spanish | null | [
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"speech",
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"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"lice... | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #es #hf-asr-leaderboard #mozilla-foundation/common_voice_6_0 #robust-speech-event #speech #xlsr-fine-tuning-week #dataset-common_voice #dataset-mozilla-foundation/common_voice_6_0 #license-apache-2.0 #model-index #endpoints_compatible #has_... | Wav2Vec2-Large-XLSR-53-Spanish
==============================
Added custom language model to URL
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Spanish using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits gener... | [] | [
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feature-extraction | transformers |
This model is created for research study which contains backdoor inside the model. Please use it for academic research, don't use it for business scenarios.
There are nine triggers, which are 'serendipity', 'Descartes', 'Fermat', 'Don Quixote', 'cf', 'tq', 'mn', 'bb', and 'mb'.
Detailed injection method can be found... | {} | Lujia/backdoored_bert | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #endpoints_compatible #region-us
|
This model is created for research study which contains backdoor inside the model. Please use it for academic research, don't use it for business scenarios.
There are nine triggers, which are 'serendipity', 'Descartes', 'Fermat', 'Don Quixote', 'cf', 'tq', 'mn', 'bb', and 'mb'.
Detailed injection method can be found... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #endpoints_compatible #region-us \n"
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29
] | [
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summarization | transformers | This is *t5-base* transformer model trained on Lithuanian news summaries for 175 000 steps.
It was created during the work [**Generating abstractive summaries of Lithuanian
news articles using a transformer model**](https://link.springer.com/chapter/10.1007/978-3-030-88304-1_27).
## Usage
```python
from transformers i... | {"language": "lt", "license": "apache-2.0", "tags": ["t5", "Lithuanian", "summarization"], "widget": [{"text": "Latvijos krep\u0161inio legenda Valdis Valteris pirmadien\u012f socialiniame tinkle pasidalino statistika, kurios vir\u0161\u016bn\u0117je yra Arvydas Sabonis. 1982 metais TSRS rinktin\u0117je debiutav\u0119s... | LukasStankevicius/t5-base-lithuanian-news-summaries-175 | null | [
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"Lithuanian",
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"autotrain_compatible",
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"lt"
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| This is *t5-base* transformer model trained on Lithuanian news summaries for 175 000 steps.
It was created during the work Generating abstractive summaries of Lithuanian
news articles using a transformer model.
## Usage
Given the following article body from 15min:
The summary can be obtained by:
Output from above w... | [
"## Usage\n\nGiven the following article body from 15min:\n\nThe summary can be obtained by:\n\nOutput from above would be:\n\nLietuvos krepšinio federacijos (LKF) prezidento Arvydo Sabonio rezultatyvumo vidurkis yra aukščiausias tarp visų Sovietų Sąjungos rinktinėje atstovavusių žaidėjų, skaičiuojant tuos, kurie s... | [
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text-generation | transformers |
# Issei Hyoudou DialoGPT Model | {"tags": ["conversational"]} | Lurka/DialoGPT-medium-isseibot | null | [
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"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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text-generation | transformers |
# Yui DialoGPT Model | {"tags": ["conversational"]} | Lurka/DialoGPT-medium-kon | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Yui DialoGPT Model | [
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text-generation | transformers |
# Tyrion DialoGPT Model | {"tags": ["conversational"]} | Luxiere/DialoGPT-medium-tyrion | null | [
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"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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text-classification | transformers |
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for BM25 retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retrievers like but using aligned BM25 works the best.
We used anserini toolkit's BM25 implementation and inde... | {"language": ["en"], "license": "apache-2.0", "tags": ["text reranking"], "datasets": ["MS MARCO document ranking"]} | Luyu/bert-base-mdoc-bm25 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"text reranking",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #text reranking #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for BM25 retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retrievers like but using aligned BM25 works the best.
We used anserini toolkit's BM25 implementation and inde... | [
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text-classification | transformers |
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for HDCT retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retrievers like BM25 but using aligned HDCT works the best.
#### How to use
See our [project repo page](https:... | {"language": ["en"], "license": "apache-2.0", "tags": ["text reranking"], "datasets": ["MS MARCO document ranking"]} | Luyu/bert-base-mdoc-hdct | null | [
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"jax",
"bert",
"text-classification",
"text reranking",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #text reranking #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT Reranker for MS-MARCO Document Ranking
## Model description
A text reranker trained for HDCT retriever on MS MARCO document dataset.
## Intended uses & limitations
It is possible to work with other retrievers like BM25 but using aligned HDCT works the best.
#### How to use
See our project repo page.
## Eva... | [
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"#### How to use\nSee our projec... | [
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null | null | It's a sentiment inference model base on bert. | {} | LzLzLz/Bert | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| It's a sentiment inference model base on bert. | [] | [
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feature-extraction | transformers | <br />
<p align="center">
<h1 align="center">M-BERT Base 69</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Base%2069">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to d... | {} | M-CLIP/M-BERT-Base-69 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| <br />
<p align="center">
<h1 align="center">M-BERT Base 69</h1>
<p align="center">
<a href="URL Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.
Once this is do... | [
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feature-extraction | transformers | <br />
<p align="center">
<h1 align="center">M-BERT Base ViT-B</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Base%20ViT-B">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you nee... | {} | M-CLIP/M-BERT-Base-ViT-B | null | [
"transformers",
"pytorch",
"tf",
"jax",
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"endpoints_compatible",
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#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #region-us
| <br />
<p align="center">
<h1 align="center">M-BERT Base ViT-B</h1>
<p align="center">
<a href="URL Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.
Once this is... | [
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feature-extraction | transformers |
<br />
<p align="center">
<h1 align="center">M-BERT Distil 40</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Distil%2040">Github Model Card</a>
</p>
</p>
## Usage
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|
<br />
<p align="center">
<h1 align="center">M-BERT Distil 40</h1>
<p align="center">
<a href="URL Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.
Once this is... | [
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feature-extraction | transformers |
<br />
<p align="center">
<h1 align="center">Swe-CLIP 2M</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/Swe-CLIP%202M">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to downloa... | {"language": "sv"} | M-CLIP/Swedish-2M | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"sv",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"sv"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #sv #endpoints_compatible #region-us
|
<br />
<p align="center">
<h1 align="center">Swe-CLIP 2M</h1>
<p align="center">
<a href="URL Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.
Once this is done... | [
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feature-extraction | transformers |
<br />
<p align="center">
<h1 align="center">Swe-CLIP 500k</h1>
<p align="center">
<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/Swe-CLIP%20500k">Github Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to dow... | {"language": "sv"} | M-CLIP/Swedish-500k | null | [
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|
<br />
<p align="center">
<h1 align="center">Swe-CLIP 500k</h1>
<p align="center">
<a href="URL Model Card</a>
</p>
</p>
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.
Once this is do... | [
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text-classification | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... | {} | M-FAC/bert-mini-finetuned-mnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"2107.03356"
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#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fine... | [] | [
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39
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text-classification | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... | {} | M-FAC/bert-mini-finetuned-mrpc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fine... | [] | [
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39
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text-classification | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... | {} | M-FAC/bert-mini-finetuned-qnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fine... | [] | [
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39
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text-classification | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on QQP dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default A... | {} | M-FAC/bert-mini-finetuned-qqp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
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#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on QQP dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we finet... | [] | [
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39
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question-answering | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison again... | {} | M-FAC/bert-mini-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2107.03356",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2107.03356 #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseli... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2107.03356 #endpoints_compatible #region-us \n"
] | [
34
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2107.03356 #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default... | {} | M-FAC/bert-mini-finetuned-sst2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fin... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-mini model finetuned with M-FAC
This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default... | {} | M-FAC/bert-mini-finetuned-stsb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-mini model finetuned with M-FAC
====================================
This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fin... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... | {} | M-FAC/bert-tiny-finetuned-mnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on MNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fine... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... | {} | M-FAC/bert-tiny-finetuned-mrpc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on MRPC dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fine... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default ... | {} | M-FAC/bert-tiny-finetuned-qnli | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on QNLI dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fine... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on QQP dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default A... | {} | M-FAC/bert-tiny-finetuned-qqp | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on QQP dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we finet... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison again... | {} | M-FAC/bert-tiny-finetuned-squadv2 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2107.03356",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #question-answering #arxiv-2107.03356 #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on SQuAD version 2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseli... | [] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2107.03356 #endpoints_compatible #region-us \n"
] | [
34
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2107.03356 #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default... | {} | M-FAC/bert-tiny-finetuned-sst2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on SST-2 dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fin... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | # BERT-tiny model finetuned with M-FAC
This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: [https://arxiv.org/pdf/2107.03356.pdf](https://arxiv.org/pdf/2107.03356.pdf).
## Finetuning setup
For fair comparison against default... | {} | M-FAC/bert-tiny-finetuned-stsb | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2107.03356",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.03356"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us
| BERT-tiny model finetuned with M-FAC
====================================
This model is finetuned on STS-B dataset with state-of-the-art second-order optimizer M-FAC.
Check NeurIPS 2021 paper for more details on M-FAC: URL
Finetuning setup
----------------
For fair comparison against default Adam baseline, we fin... | [] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-2107.03356 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Spanish News Classification Headlines
SNCH: this model was develop by [M47Labs](https://www.m47labs.com/es/) the goal is text classification, the base model use was [BETO](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased), it was fine-tuned on 1000 example dataset.
## Dataset Sample
Dataset size : ... | {"widget": [{"text": "El d\u00f3lar se dispara tras la reuni\u00f3n de la Fed"}]} | M47Labs/spanish_news_classification_headlines | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| Spanish News Classification Headlines
=====================================
SNCH: this model was develop by M47Labs the goal is text classification, the base model use was BETO, it was fine-tuned on 1000 example dataset.
Dataset Sample
--------------
Dataset size : 1000
Columns: idTask,task content 1,idTag,tag.... | [
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text-generation | transformers |
# Rick Morty DialoGPT Model | {"tags": ["conversational"]} | MAUtastic/DialoGPT-medium-RickandMortyBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Morty DialoGPT Model | [
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] |
text-generation | transformers |
# Rick Sanchez DialoGPT Model | {"tags": ["conversational"]} | MCUxDaredevil/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 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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# Rick Sanchez DialoGPT Model | [
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez DialoGPT Model"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 7121569
## Validation Metrics
- Loss: 0.2151782214641571
- Accuracy: 0.9271
- Precision: 0.9469285415796072
- Recall: 0.9051328140603155
- AUC: 0.9804569416956057
- F1: 0.925559072807107
## Usage
You can use cURL to access this model:... | {"language": "en", "tags": "autonlp", "datasets": ["MICADEE/autonlp-data-imdb-sentiment-analysis2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | MICADEE/autonlp-imdb-sentiment-analysis2-7121569 | null | [
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"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:MICADEE/autonlp-data-imdb-sentiment-analysis2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-MICADEE/autonlp-data-imdb-sentiment-analysis2 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 7121569
## Validation Metrics
- Loss: 0.2151782214641571
- Accuracy: 0.9271
- Precision: 0.9469285415796072
- Recall: 0.9051328140603155
- AUC: 0.9804569416956057
- F1: 0.925559072807107
## Usage
You can use cURL to access this model:... | [
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | MINYOUNG/distilbert-base-uncased-finetuned-cola | null | [
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"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8540
* Matthews Correlation: 0.5495
Model description
-----------------
More informa... | [
"### 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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text-classification | transformers |
# multilingual-cpv-sector-classifier
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on [the Tenders Economic Daily Public Procurement Data](https://simap.ted.europa.eu/en).
It achieves the following results on the evaluation set:
- F1 Score: 0.... | {"license": "apache-2.0", "tags": ["eu", "public procurement", "cpv", "sector", "multilingual", "transformers", "text-classification"], "widget": [{"text": "Oppeg\u00e5rd municipality, hereafter called the contracting authority, intends to enter into a framework agreement with one supplier for the procurement of fresh ... | MKaan/multilingual-cpv-sector-classifier | null | [
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"sector",
"multilingual",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #eu #public procurement #cpv #sector #multilingual #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| multilingual-cpv-sector-classifier
==================================
This model is a fine-tuned version of bert-base-multilingual-cased on the Tenders Economic Daily Public Procurement Data.
It achieves the following results on the evaluation set:
* F1 Score: 0.686
Model description
-----------------
The model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* num\\_epochs: 3\n* gradient\\_accumulation\\_steps: 8\n* batch\\_size\\_per\\_device: 4\n* total\\_train\\_batch\\_size: 32",
"### Training results"
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text2text-generation | 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. -->
# mt5-small-finetuned-pnsum2
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-pnsum2", "results": []}]} | MM98/mt5-small-finetuned-pnsum2 | null | [
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-pnsum2
==========================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Rouge1: 4.3733
* Rouge2: 1.0221
* Rougel: 4.1265
* Rougelsum: 4.1372
* Gen Len: 6.2843
Model description
------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_preci... | [
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question-answering | 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-base-spanish-wwm-cased-finetuned-spa-squad2-es-finetuned-sqac-v2
This model is a fine-tuned version of [mrm8488/bert-base-s... | {"language": ["es"], "tags": ["generated_from_trainer"], "datasets": ["sqac"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-finetuned-sqac-v2", "results": []}]} | MMG/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-finetuned-sqac | null | [
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"es"
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| bert-base-spanish-wwm-cased-finetuned-spa-squad2-es-finetuned-sqac-v2
=====================================================================
This model is a fine-tuned version of mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es on the sqac dataset.
It achieves the following results on the evaluation set:
... | [
"### 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: 3",
"### Traini... | [
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question-answering | 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-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad
This model is a fine-tuned version of [MMG/bert-base-spanish-wwm-case... | {"language": ["es"], "tags": ["generated_from_trainer"], "datasets": ["squad_es"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad", "results": []}]} | MMG/bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad | null | [
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"es"
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|
# bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad
This model is a fine-tuned version of MMG/bert-base-spanish-wwm-cased-finetuned-sqac on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5325
- {'exact_match': 60.30274361400189, 'f1': 77.01962587890856}
## Model ... | [
"# bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad\n\nThis model is a fine-tuned version of MMG/bert-base-spanish-wwm-cased-finetuned-sqac on the squad_es dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.5325\n- {'exact_match': 60.30274361400189, 'f1': 77.01962587890856}",
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question-answering | 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-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad2-es
This model is a fine-tuned version of [MMG/bert-base-spanish-wwm-... | {"language": ["es"], "tags": ["generated_from_trainer"], "datasets": ["squad_es"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad2-es", "results": []}]} | MMG/bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad2-es | null | [
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"es"
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|
# bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad2-es
This model is a fine-tuned version of MMG/bert-base-spanish-wwm-cased-finetuned-sqac on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2584
- {'exact': 63.358070500927646, 'f1': 70.22498384623977}
### Framew... | [
"# bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad2-es\n\nThis model is a fine-tuned version of MMG/bert-base-spanish-wwm-cased-finetuned-sqac on the squad_es dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2584\n- {'exact': 63.358070500927646, 'f1': 70.22498384623977}",
"... | [
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"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #es #dataset-squad_es #endpoints_compatible #region-us \n# bert-base-spanish-wwm-cased-finetuned-sqac-finetuned-squad2-es\n\nThis model is a fine-tuned version of MMG/bert-base-spanish-wwm-cased-finetuned-sqac on the squad_... |
question-answering | 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-base-spanish-wwm-cased-finetuned-sqac
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https:/... | {"language": ["es"], "tags": ["generated_from_trainer"], "datasets": ["sqac"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-sqac", "results": []}]} | MMG/bert-base-spanish-wwm-cased-finetuned-sqac | null | [
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"tensorboard",
"bert",
"question-answering",
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"dataset:sqac",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #es #dataset-sqac #endpoints_compatible #region-us
| bert-base-spanish-wwm-cased-finetuned-sqac
==========================================
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the sqac dataset.
It achieves the following results on the evaluation set:
{'exact\_match': 62.017167, 'f1': 79.452767}
Model description
-------------... | [
"### 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: 3",
"### Traini... | [
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question-answering | 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-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac
This model is a fine-tuned version of [ockapuh/bert-base-spanish-... | {"language": ["es"], "tags": ["generated_from_trainer"], "datasets": ["sqac"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac", "results": []}]} | MMG/bert-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac | null | [
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"tensorboard",
"bert",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
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#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #es #dataset-sqac #endpoints_compatible #region-us
|
# bert-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac
This model is a fine-tuned version of ockapuh/bert-base-spanish-wwm-cased-finetuned-squad2-es on the sqac dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9263
- {'exact_match': 65.55793991416309, 'f1': 82.72322701572416}
#... | [
"# bert-base-spanish-wwm-cased-finetuned-squad2-es-finetuned-sqac\n\nThis model is a fine-tuned version of ockapuh/bert-base-spanish-wwm-cased-finetuned-squad2-es on the sqac dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.9263\n- {'exact_match': 65.55793991416309, 'f1': 82.72322701572... | [
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question-answering | 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-base-spanish-wwm-cased-finetuned-squad2-es
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](ht... | {"language": ["es"], "tags": ["generated_from_trainer"], "datasets": ["squad_es"], "model-index": [{"name": "bert-base-spanish-wwm-cased-finetuned-squad2-es", "results": []}]} | MMG/bert-base-spanish-wwm-cased-finetuned-squad2-es | null | [
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# bert-base-spanish-wwm-cased-finetuned-squad2-es
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the squad_es dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2841
{'exact': 62.53162421993591, 'f1': 69.33421368741254}
### Framework versions
- Transforme... | [
"# bert-base-spanish-wwm-cased-finetuned-squad2-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the squad_es dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2841\n{'exact': 62.53162421993591, 'f1': 69.33421368741254}",
"### Framework versions\n\n-... | [
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"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #es #dataset-squad_es #endpoints_compatible #region-us \n# bert-base-spanish-wwm-cased-finetuned-squad2-es\n\nThis model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the squad_es dataset.\nIt achieves... |
fill-mask | transformers |
# mlm-spanish-roberta-base
This model has a RoBERTa base architecture and was trained from scratch with 3.6 GB of raw text over 10 epochs. 4 Tesla V-100 GPUs were used for the training.
To test the quality of the resulting model we evaluate it over the [GLUES](https://github.com/dccuchile/GLUES) benchmark for Spanis... | {"language": ["es"], "widget": [{"text": "MMG se dedica a la <mask> artificial."}]} | MMG/mlm-spanish-roberta-base | null | [
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"pytorch",
"safetensors",
"roberta",
"fill-mask",
"es",
"autotrain_compatible",
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#transformers #pytorch #safetensors #roberta #fill-mask #es #autotrain_compatible #endpoints_compatible #region-us
| mlm-spanish-roberta-base
========================
This model has a RoBERTa base architecture and was trained from scratch with 3.6 GB of raw text over 10 epochs. 4 Tesla V-100 GPUs were used for the training.
To test the quality of the resulting model we evaluate it over the GLUES benchmark for Spanish NLU. The res... | [] | [
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token-classification | transformers |
# xlm-roberta-large-ner-spanish
This model is a XLM-Roberta-large model fine-tuned for Named Entity Recognition (NER) over the Spanish portion of the CoNLL-2002 dataset. Evaluating it over the test subset of this dataset, we get a F1-score of 89.17, being one of the best NER for Spanish available at the moment. | {"language": ["es"], "datasets": ["CoNLL-2002"], "widget": [{"text": "Las oficinas de MMG est\u00e1n en Las Rozas."}]} | MMG/xlm-roberta-large-ner-spanish | null | [
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|
# xlm-roberta-large-ner-spanish
This model is a XLM-Roberta-large model fine-tuned for Named Entity Recognition (NER) over the Spanish portion of the CoNLL-2002 dataset. Evaluating it over the test subset of this dataset, we get a F1-score of 89.17, being one of the best NER for Spanish available at the moment. | [
"# xlm-roberta-large-ner-spanish\n\nThis model is a XLM-Roberta-large model fine-tuned for Named Entity Recognition (NER) over the Spanish portion of the CoNLL-2002 dataset. Evaluating it over the test subset of this dataset, we get a F1-score of 89.17, being one of the best NER for Spanish available at the moment.... | [
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null | null | # Description
A pre-trained model for volumetric (3D) segmentation of the spleen from CT image.
# Model Overview
This model is trained using the runner-up [1] awarded pipeline of the "Medical Segmentation Decathlon Challenge 2018" using the UNet architecture [2] with 32 training images and 9 validation images.
... | {"tags": ["monai"]} | MONAI/example_spleen_segmentation | null | [
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text-generation | transformers |
# Vision DialoGPT Model | {"tags": ["conversational"]} | MS366/DialoGPT-small-vision | null | [
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text2text-generation | transformers | #### Languages:
- Source language: English
- Source language: isiZulu
#### Model Details:
- model: transformer
- Architecture: MarianMT
- pre-processing: normalization + SentencePiece
#### Pre-trained Model:
- https://huggingface.co/Helsinki-NLP/opus-mt-en-xh
#### Corpus:
- Umsuka English-isiZulu Parallel Cor... | {} | MUNasir/umsuka-en-zu | null | [
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| #### Languages:
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]} | MYX4567/distilbert-base-uncased-finetuned-squad | null | [
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| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1520
Model description
-----------------
More information needed
Intended uses ... | [
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text-generation | 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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "distilgpt2-finetuned-wikitext2", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | MYX4567/distilgpt2-finetuned-wikitext2 | null | [
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| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6428
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
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text-generation | 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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following ... | {"tags": ["generated_from_trainer"], "datasets": [], "model_index": [{"name": "gpt2-wikitext2", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | MYX4567/gpt2-wikitext2 | null | [
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| gpt2-wikitext2
==============
This model is a fine-tuned version of [](URL on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 6.3227
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
... | [
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token-classification | transformers | bgc-accession model is a Named Entity Recognition (NER) model that identifies and annotates the accession number of biosynthetic gene clusters in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_bgcs_annotations
Testing examples:
1. The genom... | {} | Maaly/bgc-accession | null | [
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"token-classification",
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#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| bgc-accession model is a Named Entity Recognition (NER) model that identifies and annotates the accession number of biosynthetic gene clusters in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in URL
Testing examples:
1. The genome sequences of Leptolyngbya sp. PCC 7375 (ALVN0... | [] | [
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token-classification | transformers | body-site model is a Named Entity Recognition (NER) model that identifies and annotates the body-site of microbiome samples in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_metagenomics_annotations
Testing examples:
1. Scalp hair was colle... | {} | Maaly/body-site | null | [
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#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| body-site model is a Named Entity Recognition (NER) model that identifies and annotates the body-site of microbiome samples in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in URL
Testing examples:
1. Scalp hair was collected from behind the right ear, near the right retroaur... | [] | [
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token-classification | transformers | host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) of microbiome samples in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in https://gitlab.com/maaly7/emerald_metagenomics_annotations
Testing examples:
1. Turkestan coc... | {} | Maaly/host | null | [
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| host model is a Named Entity Recognition (NER) model that identifies and annotates the host (living organism) of microbiome samples in texts.
The model is a fine-tuned BioBERT model and the training dataset is available in URL
Testing examples:
1. Turkestan cockroach nymphs (Finke, 2013) were fed to the treefrogs a... | [] | [
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text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | MadhanKumar/DialoGPT-small-HarryPotter | null | [
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text-generation | transformers |
#Harry Potter Bot Model | {"tags": ["conversational"]} | MadhanKumar/HarryPotter-Bot | null | [
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text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 515314387
- CO2 Emissions (in grams): 70.95647633212745
## Validation Metrics
- Loss: 0.08077705651521683
- Accuracy: 0.9760103738923709
- Macro F1: 0.9728412857204902
- Micro F1: 0.9760103738923709
- Weighted F1: 0.975990715174142... | {"language": "en", "tags": "autonlp", "datasets": ["MadhurJindalWorkMail/autonlp-data-Gibb-Detect"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 70.95647633212745} | MadhurJindalWorkMail/autonlp-Gibb-Detect-515314387 | null | [
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- Problem type: Multi-class Classification
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- CO2 Emissions (in grams): 70.95647633212745
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automatic-speech-recognition | transformers | # WIP
| {} | Mads/wav2vec2-xlsr-large-53-kor-financial-engineering | null | [
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question-answering | 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. -->
# deberta-base-finetuned-squad
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deb... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "deberta-base-finetuned-squad", "results": []}]} | MaggieXM/deberta-base-finetuned-squad | null | [
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| deberta-base-finetuned-squad
============================
This model is a fine-tuned version of microsoft/deberta-base on the squad dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation dat... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | MaggieXM/distilbert-base-uncased-finetuned-squad | null | [
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"region:us"
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#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### 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: 0.01",
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text-generation | transformers | #Rick Sanchez DialoGPT Model | {"tags": "conversational"} | MagmaCubes1133/DialoGPT-large-rick | null | [
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automatic-speech-recognition | transformers |
#xlsr-large-53-tamil | {"language": ["ne"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr"], "model-index": [{"name": "wav2vec2-large-xlsr-53-tamil", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "d... | Mahalakshmi/wav2vec2-large-xlsr-53-demo-colab | null | [
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automatic-speech-recognition | 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-xls-r-300m-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-demo-colab", "results": []}]} | Mahalakshmi/wav2vec2-xls-r-300m-demo-colab | null | [
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-xls-r-300m-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9475
- eval_wer: 1.0377
- eval_runtime: 70.5646
- eval_samples_per_second: 25.239
- eval_steps_per_second: 3.16
- e... | [
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null | null | testing for nothing
| {} | Mahmoud97/Temp | null | [
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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. -->
# Persian-Image-Captioning
This model is a fine-tuned version of [Vision Encoder Decoder](https://huggingface.co/docs/transformers... | {"tags": ["generated_from_trainer"]} | MahsaShahidi/Persian-Image-Captioning | null | [
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"has_space",
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|
# Persian-Image-Captioning
This model is a fine-tuned version of Vision Encoder Decoder on coco-flickr-farsi.
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
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text-generation | transformers | ----
tags:
- conversational
---
#Peter Parker DialoGPT Model | {} | MaiaMaiaMaia/DialoGPT-medium-PeterParkerBot | null | [
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| ----
tags:
- conversational
---
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fill-mask | transformers | This model trained on nyanja dataset in Longformer | {} | MalawiUniST/ISO6392.nya.ny | null | [
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"longformer",
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"autotrain_compatible",
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null | null | Ver-Online Malignant PELICULA completa En Espanol Latino HD | {} | Malignant/Malignant | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Ver-Online Malignant PELICULA completa En Espanol Latino HD | [] | [
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5
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token-classification | transformers |
# Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient m... | {"language": "da", "license": "mit", "tags": ["\u00e6l\u00e6ctra", "pytorch", "danish", "ELECTRA-Small", "replaced token detection"], "datasets": ["DAGW"], "metrics": ["f1"], "widget": [{"text": "Chili Jensen, som bor p\u00e5 Danmarksgade 12, k\u00f8ber chilifrugter fra Netto."}]} | Maltehb/aelaectra-danish-electra-small-cased-ner-dane | null | [
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| Ælæctra - Finetuned for Named Entity Recognition on the DaNE dataset (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
=========================================================================================================================
Ælæctra is a Danish Transformer-based language model created to enhance th... | [
"### Evaluation of current Danish Language Models\n\n\nÆlæctra, Danish BERT (DaBERT) and multilingual BERT (mBERT) were evaluated:\n\n\n\nOn DaNE (Hvingelby et al., 2020) without the *MISC-tag*, Ælæctra scores slightly worse than both cased and uncased Multilingual BERT (Devlin et al., 2019) and Danish BERT (Danish... | [
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null | transformers |
# Ælæctra - A Step Towards More Efficient Danish Natural Language Processing
**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. Initially a cased and an uncased model are releas... | {"language": "da", "license": "mit", "tags": ["\u00e6l\u00e6ctra", "pytorch", "danish", "ELECTRA-Small", "replaced token detection"], "datasets": ["DAGW"], "metrics": ["f1"], "co2_eq_emissions": 4009.5} | Maltehb/aelaectra-danish-electra-small-cased | null | [
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| Ælæctra - A Step Towards More Efficient Danish Natural Language Processing
==========================================================================
Ælæctra is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-th... | [
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token-classification | transformers |
# Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient m... | {"language": "da", "license": "mit", "tags": ["\u00e6l\u00e6ctra", "pytorch", "danish", "ELECTRA-Small", "replaced token detection"], "datasets": ["DAGW"], "metrics": ["f1"], "widget": [{"text": "Chili Jensen, som bor p\u00e5 Danmarksgade 12, k\u00f8ber chilifrugter fra Netto."}]} | Maltehb/aelaectra-danish-electra-small-uncased-ner-dane | null | [
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| Ælæctra - Finetuned for Named Entity Recognition on the DaNE dataset (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
=========================================================================================================================
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null | transformers |
# Ælæctra - A Step Towards More Efficient Danish Natural Language Processing
**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models. Initially a cased and an uncased model are releas... | {"language": "da", "license": "mit", "tags": ["\u00e6l\u00e6ctra", "pytorch", "danish", "ELECTRA-Small", "replaced token detection"], "datasets": ["DAGW"], "metrics": ["f1"], "co2_eq_emissions": 4009.5} | Maltehb/aelaectra-danish-electra-small-uncased | null | [
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| Ælæctra - A Step Towards More Efficient Danish Natural Language Processing
==========================================================================
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token-classification | transformers |
# Danish BERT (version 2, uncased) by [Certainly](https://certainly.io/) (previously known as BotXO) finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
Humongous amounts of credit needs to go to [C... | {"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "masked-lm", "botxo"], "datasets": ["common_crawl", "wikipedia", "dindebat.dk", "hestenettet.dk", "danish_OpenSubtitles"], "widget": [{"text": "Chili Jensen, som bor p\u00e5 Danmarksgade 12, k\u00f8ber chilifrugter fra Netto."}]} | Maltehb/danish-bert-botxo-ner-dane | null | [
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"autotrain_compatible",
"... | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# Danish BERT (version 2, uncased) by Certainly (previously known as BotXO) finetuned for Named Entity Recognition on the DaNE dataset (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
Humongous amounts of credit needs to go to Certainly (previously known as BotXO), for pretraining the Danish BERT. For data and tr... | [
"# Danish BERT (version 2, uncased) by Certainly (previously known as BotXO) finetuned for Named Entity Recognition on the DaNE dataset (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.\n\nHumongous amounts of credit needs to go to Certainly (previously known as BotXO), for pretraining the Danish BERT. For data ... | [
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fill-mask | transformers |
# Danish BERT (version 2, uncased) by [Certainly](https://certainly.io/) (previously known as BotXO).
All credit goes to [Certainly](https://certainly.io/) (previously known as BotXO), who developed Danish BERT. For data and training details see their [GitHub repository](https://github.com/certainlyio/nordic_bert) or... | {"language": "da", "license": "cc-by-4.0", "tags": ["danish", "bert", "masked-lm", "Certainly"], "datasets": ["common_crawl", "wikipedia", "dindebat.dk", "hestenettet.dk", "danishOpenSubtitles"], "pipeline_tag": "fill-mask", "widget": [{"text": "K\u00f8benhavn er [MASK] i Danmark."}]} | Maltehb/danish-bert-botxo | null | [
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"license:cc-by-4.0",
"autotrai... | null | 2022-03-02T23:29:04+00:00 | [] | [
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|
# Danish BERT (version 2, uncased) by Certainly (previously known as BotXO).
All credit goes to Certainly (previously known as BotXO), who developed Danish BERT. For data and training details see their GitHub repository or this article. You can also visit their organization page on Hugging Face.
It is both available... | [
"# Danish BERT (version 2, uncased) by Certainly (previously known as BotXO).\n\nAll credit goes to Certainly (previously known as BotXO), who developed Danish BERT. For data and training details see their GitHub repository or this article. You can also visit their organization page on Hugging Face.\n\nIt is both a... | [
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text-generation | transformers | hello
| {} | Mamatha/agri-gpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| hello
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text-generation | transformers |
#Mikasa Ackermann DialoGPT Model | {"tags": ["conversational"]} | Mandy/DialoGPT-small-Mikasa | null | [
"transformers",
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"text-generation",
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"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
#Mikasa Ackermann DialoGPT Model | [] | [
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39
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] |
automatic-speech-recognition | 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | Maniac/wav2vec2-xls-r-60-urdu | null | [
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"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
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"ur",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ur"
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|
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 3.8433
* Wer: 0.9852
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* ... | [
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automatic-speech-recognition | 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)... | {"language": ["ur"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "sv", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "", "results": [{"ta... | Maniac/wav2vec2-xls-r-urdu | null | [
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"ur",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2... | null | 2022-03-02T23:29:04+00:00 | [] | [
"ur"
] | TAGS
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|
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - UR dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5614
* Wer: 0.6765
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\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* training\\_steps: 1000\n* mixed... | [
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"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #sv #robust-speech-event #model_for_talk #hf-asr-leaderboard #ur #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n### Tra... |
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