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text-generation | transformers |
#Harry Potter DialGPT Model | {"tags": ["conversational"]} | maxxx2021/DialGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | transformers |
# PEGASUS for COVID Literature Summarization
## Model Description
Pegasus-large fine-tuned for COVID literature summarization
## Training data
The data is the [CORD-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) dataset, containing over 400,000 scholarly articles, including over 150,... | {"language": "en", "tags": ["pytorch", "pegasus", "summarization"], "datasets": ["CORD-19"], "widget": [{"text": "Background: On 31 December 2019, the World Health Organization was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The causative pathogen was suspected to be a virus, but it di... | mayu0007/pegasus_large_covid | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"summarization",
"en",
"dataset:CORD-19",
"arxiv:1912.08777",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.08777"
] | [
"en"
] | TAGS
#transformers #pytorch #pegasus #text2text-generation #summarization #en #dataset-CORD-19 #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #region-us
| PEGASUS for COVID Literature Summarization
==========================================
Model Description
-----------------
Pegasus-large fine-tuned for COVID literature summarization
Training data
-------------
The data is the CORD-19 dataset, containing over 400,000 scholarly articles, including over 150,000 wi... | [
"### How to use\n\n\nWe provide a simple snippet of how to use this model for the task of text summarization in PyTorch."
] | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #summarization #en #dataset-CORD-19 #arxiv-1912.08777 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nWe provide a simple snippet of how to use this model for the task of text summarization in PyTorch."
] |
text-classification | transformers |
# Prediction of sentence "nature" in a French political sentence
This model aims at predicting the nature of a sentence in a French political sentence.
The predictions fall in three categories:
- `problem`: the sentence describes a problem (usually to be tackled by the speaker), for example _il y a dans ce pays une f... | {"language": "fr", "tags": ["autonlp", "Text Classification", "Politics"], "datasets": ["mazancourt/autonlp-data-politics-sentence-classifier"], "widget": [{"text": "Il y a dans ce pays une fracture"}], "co2_eq_emissions": 1.06099358268878} | mazancourt/politics-sentence-classifier | null | [
"transformers",
"pytorch",
"safetensors",
"camembert",
"text-classification",
"autonlp",
"Text Classification",
"Politics",
"fr",
"dataset:mazancourt/autonlp-data-politics-sentence-classifier",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #safetensors #camembert #text-classification #autonlp #Text Classification #Politics #fr #dataset-mazancourt/autonlp-data-politics-sentence-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Prediction of sentence "nature" in a French political sentence
This model aims at predicting the nature of a sentence in a French political sentence.
The predictions fall in three categories:
- 'problem': the sentence describes a problem (usually to be tackled by the speaker), for example _il y a dans ce pays une f... | [
"# Prediction of sentence \"nature\" in a French political sentence\n\nThis model aims at predicting the nature of a sentence in a French political sentence.\nThe predictions fall in three categories:\n- 'problem': the sentence describes a problem (usually to be tackled by the speaker), for example _il y a dans ce ... | [
"TAGS\n#transformers #pytorch #safetensors #camembert #text-classification #autonlp #Text Classification #Politics #fr #dataset-mazancourt/autonlp-data-politics-sentence-classifier #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Prediction of sentence \"nature\" in a French politi... |
null | null | Model weight for Fast Style Transfer
```
class TransformerNetwork(nn.Module):
"""Feedforward Transformation Network without Tanh
reference: https://arxiv.org/abs/1603.08155
exact architecture: https://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf
"""
def __init__(s... | {"license": "mit"} | maze/FastStyleTransfer | null | [
"arxiv:1603.08155",
"arxiv:1512.03385",
"license:mit",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1603.08155",
"1512.03385"
] | [] | TAGS
#arxiv-1603.08155 #arxiv-1512.03385 #license-mit #has_space #region-us
| Model weight for Fast Style Transfer
| [] | [
"TAGS\n#arxiv-1603.08155 #arxiv-1512.03385 #license-mit #has_space #region-us \n"
] |
null | null | readme test | {} | mazula/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| readme test | [] | [
"TAGS\n#region-us \n"
] |
question-answering | transformers |
# Model Overview
This is an ELECTRA-Large QA Model trained from https://huggingface.co/google/electra-large-discriminator in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/abs/2002.0029... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["adversarial_qa", "mbartolo/synQA", "squad"], "metrics": ["exact_match", "f1"], "model-index": [{"name": "mbartolo/electra-large-synqa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset"... | mbartolo/electra-large-synqa | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"en",
"dataset:adversarial_qa",
"dataset:mbartolo/synQA",
"dataset:squad",
"arxiv:2002.00293",
"arxiv:2104.08678",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.00293",
"2104.08678"
] | [
"en"
] | TAGS
#transformers #pytorch #electra #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Model Overview
This is an ELECTRA-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning.
# Data
Training data: SQuAD + AdversarialQA
Ev... | [
"# Model Overview\nThis is an ELECTRA-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning.",
"# Data\nTraining data: SQuAD + Advers... | [
"TAGS\n#transformers #pytorch #electra #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Model Overview\nThis is an ELECTRA-Large QA Model trained from URL in two stag... |
question-answering | transformers |
# Model Overview
This is a RoBERTa-Large QA Model trained from https://huggingface.co/roberta-large in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD as well as Wikipedia passages external to SQuAD, and then it is trained ... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["adversarial_qa", "mbartolo/synQA", "squad"], "metrics": ["exact_match", "f1"], "model-index": [{"name": "mbartolo/roberta-large-synqa-ext", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "data... | mbartolo/roberta-large-synqa-ext | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:adversarial_qa",
"dataset:mbartolo/synQA",
"dataset:squad",
"arxiv:2002.00293",
"arxiv:2104.08678",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.00293",
"2104.08678"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Model Overview
This is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD as well as Wikipedia passages external to SQuAD, and then it is trained on SQuAD and AdversarialQA (URL i... | [
"# Model Overview\nThis is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD as well as Wikipedia passages external to SQuAD, and then it is trained on SQuAD and AdversarialQA (... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Model Overview\nThis is a RoBERTa-Large QA Model trained from URL in two stage... |
question-answering | transformers |
# Model Overview
This is a RoBERTa-Large QA Model trained from https://huggingface.co/roberta-large in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (https://arxiv.org/ab... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["adversarial_qa", "mbartolo/synQA", "squad"], "metrics": ["exact_match", "f1"], "model-index": [{"name": "mbartolo/roberta-large-synqa", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset"... | mbartolo/roberta-large-synqa | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"en",
"dataset:adversarial_qa",
"dataset:mbartolo/synQA",
"dataset:squad",
"arxiv:2002.00293",
"arxiv:2104.08678",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2002.00293",
"2104.08678"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Model Overview
This is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning.
# Data
Trainin... | [
"# Model Overview\nThis is a RoBERTa-Large QA Model trained from URL in two stages. First, it is trained on synthetic adversarial data generated using a BART-Large question generator on Wikipedia passages from SQuAD, and then it is trained on SQuAD and AdversarialQA (URL in a second stage of fine-tuning.",
"# Dat... | [
"TAGS\n#transformers #pytorch #roberta #question-answering #en #dataset-adversarial_qa #dataset-mbartolo/synQA #dataset-squad #arxiv-2002.00293 #arxiv-2104.08678 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Model Overview\nThis is a RoBERTa-Large QA Model trained from URL in two stage... |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | mbateman/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0622
* Precision: 0.9334
* Recall: 0.9498
* F1: 0.9415
* Accuracy: 0.9868
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | mbateman/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4033
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_si... |
question-answering | transformers |
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1)... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering"], "datasets": ["squad"], "metrics": ["squad"], "thumbnail": "https://github.com/karanchahal/distiller/blob/master/distiller.jpg"} | mbateman/distilbert-base-uncased-finetuned-squad-d5716d28 | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| DistilBERT with a second step of distillation
=============================================
Model description
-----------------
This model replicates the "DistilBERT (D)" model from Table 2 of the DistilBERT paper. In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-... | [
"### BibTeX entry and citation info"
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #question-answering #en #dataset-squad #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### BibTeX entry and citation info"
] |
translation | 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["translation", "generated_from_trainer"], "datasets": ["kde4"], "model-index": [{"name": "marian-finetuned-kde4-en-to-fr", "results": []}]} | mbateman/marian-finetuned-kde4-en-to-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the kde4 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# marian-finetuned-kde4-en-to-fr\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-fr on the k... |
summarization | 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-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | mbateman/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0393
* Rouge1: 17.3313
* Rouge2: 8.1251
* Rougel: 17.0359
* Rougelsum: 16.9503
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-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: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
token-classification | transformers |
# xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-amharic](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-amharic) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Amha... | {"language": ["am"], "tags": ["NER", "token-classification"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "\u1240\u12f3\u121a\u12cd \u12e8\u1236\u121b\u120c \u12ad\u120d\u120d \u1260\u12a0\u12c8\u12f3\u12ed \u12a8\u1270\u121b \u1208\u1270\u1308\u12f0\u1209 \u12e8\u12ad\u120... | mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"am",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"am"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #am #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-amharic-finetuned-ner-amharic
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-amharic on the MasakhaNER dataset, specifically the Amharic part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #am #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-amharic](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-amharic) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swah... | {"language": ["sw"], "tags": ["NER", "token-classification"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-amharic-finetuned-ner-swahili
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-amharic on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-hausa-finetuned-ner-hausa
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-hausa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-hausa) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Hausa part.
... | {"language": ["ha"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "A saurari cikakken rahoton wakilin Muryar Amurka Ibrahim Abdul'aziz"}]} | mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-hausa | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"ha",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"ha"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #ha #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-hausa-finetuned-ner-hausa
====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-hausa on the MasakhaNER dataset, specifically the Hausa part.
More information, and other similar models can be... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #ha #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-hausa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-hausa) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili pa... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-hausa on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar models ... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-igbo-finetuned-ner-igbo
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-igbo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-igbo) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Igbo part.
More... | {"language": ["ig"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ike \u1ecbda j\u1ee5\u1ee5 ot\u1ee5 nkeji banyere oke ogbugbu na - eme n'ala Naijiria agw\u1ee5la Ekweremmad\u1ee5"}]} | mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-igbo | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"ig",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"ig"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #ig #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-igbo-finetuned-ner-igbo
==================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-igbo on the MasakhaNER dataset, specifically the Igbo part.
More information, and other similar models can be found... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #ig #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-igbo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-igbo) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili part.... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili
=====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-igbo on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar models can... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-kinyarwanda
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-kinyarwanda](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-kinyarwanda) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, speci... | {"language": ["rw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi wa EU mu Rwanda , Nicola Bellomo yagize ati \u201c Inkunga yacu ni imwe mu nkunga yagutse yiswe # TeamEurope ."}]} | mbeukman/xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-kinyarwanda | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"rw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"rw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #rw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-kinyarwanda
================================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-kinyarwanda on the MasakhaNER dataset, specifically the Kinyarwanda part.
More informat... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #rw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-kinyarwanda](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-kinyarwanda) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifica... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili
============================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-kinyarwanda on the MasakhaNER dataset, specifically the Swahili part.
More information, and oth... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-luganda-finetuned-ner-luganda
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-luganda](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luganda) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the luga... | {"language": ["lug"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Empaka zaakubeera mu kibuga Liverpool e Bungereza , okutandika nga July 12 ."}]} | mbeukman/xlm-roberta-base-finetuned-luganda-finetuned-ner-luganda | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"lug",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"lug"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #lug #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-luganda-finetuned-ner-luganda
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-luganda on the MasakhaNER dataset, specifically the luganda part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #lug #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-luganda](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luganda) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swah... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-luganda on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-luo-finetuned-ner-luo
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-luo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luo) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Luo part.
More info... | {"language": ["luo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "\ufeffJii 2 moko jowito ngimagi ka machielo 1 to ohinyore marach mokalo e masira makoch mar apaya mane otimore e apaya mawuok Oyugis kochimo Chabera e sub county ma Rachuonyo East e County m... | mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-luo | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"luo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"luo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #luo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-luo-finetuned-ner-luo
================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-luo on the MasakhaNER dataset, specifically the Luo part.
More information, and other similar models can be found in th... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #luo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-luo-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-luo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luo) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili part.
M... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-luo-finetuned-ner-swahili
====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-luo on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar models can be... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-naija-finetuned-ner-naija
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-naija](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-naija) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Nigerian Pid... | {"language": ["pcm"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi , Ultimate Fighting Championship , UFC don decide say dem go enta back di octagon on Saturday , 9 May , for Jacksonville , Florida ."}]} | mbeukman/xlm-roberta-base-finetuned-naija-finetuned-ner-naija | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"pcm"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #pcm #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-naija-finetuned-ner-naija
====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-naija on the MasakhaNER dataset, specifically the Nigerian Pidgin part.
More information, and other similar mod... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #pcm #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-naija-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-naija](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-naija) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili pa... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-naija-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-naija-finetuned-ner-swahili
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-naija on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar models ... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-amharic
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Amharic part.
More information, and other similar models can be ... | {"language": ["am"], "tags": ["NER", "token-classification"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "\u1240\u12f3\u121a\u12cd \u12e8\u1236\u121b\u120c \u12ad\u120d\u120d \u1260\u12a0\u12c8\u12f3\u12ed \u12a8\u1270\u121b \u1208\u1270\u1308\u12f0\u1209 \u12e8\u12ad\u120... | mbeukman/xlm-roberta-base-finetuned-ner-amharic | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"am",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"am"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #am #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-amharic
======================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Amharic part.
More information, and other similar models can be found in the main Github repository.
Ab... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #am #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-hausa
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Hausa part.
More information, and other similar models can be foun... | {"language": ["ha"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "A saurari cikakken rahoton wakilin Muryar Amurka Ibrahim Abdul'aziz"}]} | mbeukman/xlm-roberta-base-finetuned-ner-hausa | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"ha",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"ha"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #ha #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-hausa
====================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Hausa part.
More information, and other similar models can be found in the main Github repository.
About
--... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #ha #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-igbo
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Igbo part.
More information, and other similar models can be found ... | {"language": ["ig"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ike \u1ecbda j\u1ee5\u1ee5 ot\u1ee5 nkeji banyere oke ogbugbu na - eme n'ala Naijiria agw\u1ee5la Ekweremmad\u1ee5"}]} | mbeukman/xlm-roberta-base-finetuned-ner-igbo | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"ig",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"ig"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #ig #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-igbo
===================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Igbo part.
More information, and other similar models can be found in the main Github repository.
About
-----... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #ig #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-kinyarwanda
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Kinyarwanda part.
More information, and other similar models... | {"language": ["rw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi wa EU mu Rwanda , Nicola Bellomo yagize ati \u201c Inkunga yacu ni imwe mu nkunga yagutse yiswe # TeamEurope ."}]} | mbeukman/xlm-roberta-base-finetuned-ner-kinyarwanda | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"rw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"rw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #rw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #has_space #region-us
| xlm-roberta-base-finetuned-ner-kinyarwanda
==========================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Kinyarwanda part.
More information, and other similar models can be found in the main Github repo... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #rw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and furthe... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-luganda
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the luganda part.
More information, and other similar models can be ... | {"language": ["lug"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Empaka zaakubeera mu kibuga Liverpool e Bungereza , okutandika nga July 12 ."}]} | mbeukman/xlm-roberta-base-finetuned-ner-luganda | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"lug",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"lug"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #lug #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-luganda
======================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the luganda part.
More information, and other similar models can be found in the main Github repository.
Ab... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #lug #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-luo
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Luo part.
More information, and other similar models can be found in... | {"language": ["luo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "\ufeffJii 2 moko jowito ngimagi ka machielo 1 to ohinyore marach mokalo e masira makoch mar apaya mane otimore e apaya mawuok Oyugis kochimo Chabera e sub county ma Rachuonyo East e County m... | mbeukman/xlm-roberta-base-finetuned-ner-luo | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"luo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"luo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #luo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-luo
==================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Luo part.
More information, and other similar models can be found in the main Github repository.
About
-----
... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #luo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-naija
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Nigerian Pidgin part.
More information, and other similar models c... | {"language": ["pcm"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi , Ultimate Fighting Championship , UFC don decide say dem go enta back di octagon on Saturday , 9 May , for Jacksonville , Florida ."}]} | mbeukman/xlm-roberta-base-finetuned-ner-naija | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"pcm"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #pcm #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-naija
====================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Nigerian Pidgin part.
More information, and other similar models can be found in the main Github repository.
... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #pcm #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili part.
More information, and other similar models can be ... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-swahili
======================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar models can be found in the main Github repository.
Ab... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-wolof
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Wolof part.
More information, and other similar models can be foun... | {"language": ["wo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "SAFIYETU B\u00c9EY C\u00e9y Koronaa !"}]} | mbeukman/xlm-roberta-base-finetuned-ner-wolof | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"wo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"wo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #wo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-wolof
====================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Wolof part.
More information, and other similar models can be found in the main Github repository.
About
--... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #wo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-ner-yoruba
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Yoruba part.
More information, and other similar models can be fo... | {"language": ["yo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "K\u00f2 s\u00ed \u1eb9\u0300r\u00ed t\u00ed \u00f3 fi \u1eb9s\u1eb9\u0300 rinl\u1eb9\u0300 ."}]} | mbeukman/xlm-roberta-base-finetuned-ner-yoruba | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"yo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"yo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #yo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-ner-yoruba
=====================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base on the MasakhaNER dataset, specifically the Yoruba part.
More information, and other similar models can be found in the main Github repository.
About... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #yo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Amha... | {"language": ["am"], "tags": ["NER", "token-classification"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "\u1240\u12f3\u121a\u12cd \u12e8\u1236\u121b\u120c \u12ad\u120d\u120d \u1260\u12a0\u12c8\u12f3\u12ed \u12a8\u1270\u121b \u1208\u1270\u1308\u12f0\u1209 \u12e8\u12ad\u120... | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"am",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"am"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #am #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-amharic
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Amharic part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #am #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-hausa
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Hausa ... | {"language": ["ha"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "A saurari cikakken rahoton wakilin Muryar Amurka Ibrahim Abdul'aziz"}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-hausa | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"ha",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"ha"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #ha #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-hausa
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Hausa part.
More information, and other similar models ... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #ha #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-igbo
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Igbo pa... | {"language": ["ig"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ike \u1ecbda j\u1ee5\u1ee5 ot\u1ee5 nkeji banyere oke ogbugbu na - eme n'ala Naijiria agw\u1ee5la Ekweremmad\u1ee5"}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-igbo | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"ig",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"ig"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #ig #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-igbo
=====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Igbo part.
More information, and other similar models can... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #ig #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-kinyarwanda
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the ... | {"language": ["rw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Ambasaderi wa EU mu Rwanda , Nicola Bellomo yagize ati \u201c Inkunga yacu ni imwe mu nkunga yagutse yiswe # TeamEurope ."}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-kinyarwanda | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"rw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"rw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #rw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-kinyarwanda
============================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Kinyarwanda part.
More information, and oth... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #rw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-luganda
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the luga... | {"language": ["lug"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Empaka zaakubeera mu kibuga Liverpool e Bungereza , okutandika nga July 12 ."}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-luganda | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"lug",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"lug"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #lug #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-luganda
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the luganda part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #lug #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-luo
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Luo part... | {"language": ["luo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "\ufeffJii 2 moko jowito ngimagi ka machielo 1 to ohinyore marach mokalo e masira makoch mar apaya mane otimore e apaya mawuok Oyugis kochimo Chabera e sub county ma Rachuonyo East e County m... | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-luo | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"luo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"luo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #luo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-luo
====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Luo part.
More information, and other similar models can be... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #luo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-naija
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Nigeri... | {"language": ["pcm"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Mixed Martial Arts joinbodi , Ultimate Fighting Championship , UFC don decide say dem go enta back di octagon on Saturday , 9 May , for Jacksonville , Florida ."}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-naija | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"pcm",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"pcm"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #pcm #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-naija
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Nigerian Pidgin part.
More information, and other simil... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #pcm #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resource... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swah... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili
========================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar m... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Wolof ... | {"language": ["wo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "SAFIYETU B\u00c9EY C\u00e9y Koronaa !"}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"wo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"wo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #wo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Wolof part.
More information, and other similar models ... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #wo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-swahili-finetuned-ner-yoruba
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-swahili](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Yorub... | {"language": ["yo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "K\u00f2 s\u00ed \u1eb9\u0300r\u00ed t\u00ed \u00f3 fi \u1eb9s\u1eb9\u0300 rinl\u1eb9\u0300 ."}]} | mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-yoruba | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"yo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"yo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #yo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-swahili-finetuned-ner-yoruba
=======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-swahili on the MasakhaNER dataset, specifically the Yoruba part.
More information, and other similar mode... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #yo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-wolof](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili pa... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-wolof on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar models ... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-wolof](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Wolof part.
... | {"language": ["wo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "SAFIYETU B\u00c9EY C\u00e9y Koronaa !"}]} | mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"wo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"wo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #wo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof
====================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-wolof on the MasakhaNER dataset, specifically the Wolof part.
More information, and other similar models can be... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #wo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-yoruba](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-yoruba) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili... | {"language": ["sw"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."}]} | mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"sw"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili
=======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-yoruba on the MasakhaNER dataset, specifically the Swahili part.
More information, and other similar mode... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #sw #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
token-classification | transformers |
# xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-yoruba](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-yoruba) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Yoruba p... | {"language": ["yo"], "tags": ["NER"], "datasets": ["masakhaner"], "metrics": ["f1", "precision", "recall"], "widget": [{"text": "K\u00f2 s\u00ed \u1eb9\u0300r\u00ed t\u00ed \u00f3 fi \u1eb9s\u1eb9\u0300 rinl\u1eb9\u0300 ."}]} | mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"NER",
"yo",
"dataset:masakhaner",
"arxiv:2103.11811",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.11811"
] | [
"yo"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #NER #yo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba
======================================================
This is a token classification (specifically NER) model that fine-tuned xlm-roberta-base-finetuned-yoruba on the MasakhaNER dataset, specifically the Yoruba part.
More information, and other similar models ... | [
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources, you can visit the the main Github repository. You can contact me by filing an issue on this repository.",
"### Training Resources\n\n\nIn the interest of openness, and re... | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #NER #yo #dataset-masakhaner #arxiv-2103.11811 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Contact & More information\n\n\nFor more information about the models, including training scripts, detailed results and further resources... |
text-generation | transformers | # fdh-wikibio
Model used to prepare Biography Generator for EPFL Foundations of Digital Humanities course.
## Project description
Please read our report on FDH page: http://fdh.epfl.ch/index.php/WikiBio
## Project result
You're invited to read through our generated biographies!
https://wikibio.mbien.pl/ | {} | mbien/fdh-wikibio | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # fdh-wikibio
Model used to prepare Biography Generator for EPFL Foundations of Digital Humanities course.
## Project description
Please read our report on FDH page: URL
## Project result
You're invited to read through our generated biographies!
URL | [
"# fdh-wikibio\n\nModel used to prepare Biography Generator for EPFL Foundations of Digital Humanities course.",
"## Project description\nPlease read our report on FDH page: URL",
"## Project result\nYou're invited to read through our generated biographies!\nURL"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# fdh-wikibio\n\nModel used to prepare Biography Generator for EPFL Foundations of Digital Humanities course.",
"## Project description\nPlease read our ... |
feature-extraction | transformers | # Predicting music popularity using DNNs
This is a pre-trained wav2vec2.0 model, trained on a fill Free Music Archive repository, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa (elisa.michelet@epfl.ch)
* Michał (michal.bien@epfl.ch)
* Noé (noe.durandard@epfl.ch)
## Milestone 3
Main not... | {} | mbien/fma2vec | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #feature-extraction #endpoints_compatible #has_space #region-us
| # Predicting music popularity using DNNs
This is a pre-trained wav2vec2.0 model, trained on a fill Free Music Archive repository, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa (elisa.michelet@URL)
* Michał (URL@URL)
* Noé (noe.durandard@URL)
## Milestone 3
Main notebook presenting out... | [
"# Predicting music popularity using DNNs\n\nThis is a pre-trained wav2vec2.0 model, trained on a fill Free Music Archive repository, created as part of DH-401: Digital Musicology class on EPFL",
"## Team\n\n* Elisa (elisa.michelet@URL)\n* Michał (URL@URL)\n* Noé (noe.durandard@URL)",
"## Milestone 3\n\nMain no... | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2 #feature-extraction #endpoints_compatible #has_space #region-us \n",
"# Predicting music popularity using DNNs\n\nThis is a pre-trained wav2vec2.0 model, trained on a fill Free Music Archive repository, created as part of DH-401: Digital Musicology class on EPF... |
null | transformers | # Predicting music popularity using DNNs
This is a model fine-tuned for music popularity classification, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa (elisa.michelet@epfl.ch)
* Michał (michal.bien@epfl.ch)
* Noé (noe.durandard@epfl.ch)
## Milestone 3
Main notebook presenting out resu... | {} | mbien/fma2vec2popularity | null | [
"transformers",
"pytorch",
"wav2vec2",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #endpoints_compatible #region-us
| # Predicting music popularity using DNNs
This is a model fine-tuned for music popularity classification, created as part of DH-401: Digital Musicology class on EPFL
## Team
* Elisa (elisa.michelet@URL)
* Michał (URL@URL)
* Noé (noe.durandard@URL)
## Milestone 3
Main notebook presenting out results is available her... | [
"# Predicting music popularity using DNNs\n\nThis is a model fine-tuned for music popularity classification, created as part of DH-401: Digital Musicology class on EPFL",
"## Team\n\n* Elisa (elisa.michelet@URL)\n* Michał (URL@URL)\n* Noé (noe.durandard@URL)",
"## Milestone 3\n\nMain notebook presenting out res... | [
"TAGS\n#transformers #pytorch #wav2vec2 #endpoints_compatible #region-us \n",
"# Predicting music popularity using DNNs\n\nThis is a model fine-tuned for music popularity classification, created as part of DH-401: Digital Musicology class on EPFL",
"## Team\n\n* Elisa (elisa.michelet@URL)\n* Michał (URL@URL)\n*... |
text-generation | transformers | # RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
Model accompanying our INLG 2020 paper: [RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation](https://www.aclweb.org/anthology/2020.inlg-1.4.pdf)
## Where is the dataset?
Please visit the website of our project: [recipenl... | {} | mbien/recipenlg | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
Model accompanying our INLG 2020 paper: RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation
## Where is the dataset?
Please visit the website of our project: URL to download it.
## How to use the model? Could you explain... | [
"# RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation\n\nModel accompanying our INLG 2020 paper: RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation",
"## Where is the dataset?\n\nPlease visit the website of our project: URL to download it.",
"## How to use the model? ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation\n\nModel accompanying our INLG 2020 paper: RecipeNLG: A Cooking Recip... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Polish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The mod... | {"language": "pl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "mbien/wav2vec2-large-xlsr-polish", "results": [{"task": {"type": ... | mbien/wav2vec2-large-xlsr-polish | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"pl",
"dataset:common_voice",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pl"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #pl #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Polish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the Common Voice dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluat... | [
"# Wav2Vec2-Large-XLSR-53-Polish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe mode... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #pl #dataset-common_voice #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Polish\n\nFine-tuned facebook/wav2... |
sentence-similarity | sentence-transformers |
# mboth/distil-eng-quora-sentence
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | mboth/distil-eng-quora-sentence | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us
|
# mboth/distil-eng-quora-sentence
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed... | [
"# mboth/distil-eng-quora-sentence\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #has_space #region-us \n",
"# mboth/distil-eng-quora-sentence\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be ... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can... | {"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Mehmet Berk Souksu", "results": [{"task": {"type": "automatic-speech-recognition", "name"... | mbsouksu/wav2vec2-large-xlsr-turkish-large | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as ... | [
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can ... | [
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Turkish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Turkish using the Com... |
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. -->
# bert-base-NER-finetuned-ner
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-N... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-NER-finetuned-ner", "results": []}]} | mcdzwil/bert-base-NER-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-NER-finetuned-ner
===========================
This model is a fine-tuned version of dslim/bert-base-NER on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1670
* Precision: 0.8358
* Recall: 0.7615
* F1: 0.7969
* Accuracy: 0.9437
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... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size:... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": []}]} | mcdzwil/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1830
* Precision: 0.9171
* Recall: 0.7099
* F1: 0.8003
* Accuracy: 0.9316
Model descript... | [
"### 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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_... |
image-classification | transformers |
### Model Description
The ***ResNet50 v1.5*** model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385).
The difference between v1 and v1.5 is that, in the bottleneck blocks which requires
downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = ... | {"license": "apache-2.0", "tags": ["image-classification", "resnet"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_ti... | mchochowski/test-model | null | [
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"image-classification",
"resnet",
"dataset:imagenet",
"arxiv:1512.03385",
"arxiv:1502.01852",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1512.03385",
"1502.01852"
] | [] | TAGS
#transformers #image-classification #resnet #dataset-imagenet #arxiv-1512.03385 #arxiv-1502.01852 #license-apache-2.0 #endpoints_compatible #region-us
|
### Model Description
The *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.
The difference between v1 and v1.5 is that, in the bottleneck blocks which requires
downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.
This differen... | [
"### Model Description\n\nThe *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.\n\nThe difference between v1 and v1.5 is that, in the bottleneck blocks which requires\ndownsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.\n\nTh... | [
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"### Model Description\n\nThe *ResNet50 v1.5* model is a modified version of the original ResNet50 v1 model.\n\nThe difference between v1 and v1.5 is t... |
null | null |
# 42 is the answer
Don't forget your towel !
## Panic
Don't
## Happiness
Is more important than being right | {"license": "mit"} | mcpotato/42 | null | [
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# 42 is the answer
Don't forget your towel !
## Panic
Don't
## Happiness
Is more important than being right | [
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"## Panic\n\nDon't",
"## Happiness\n\nIs more important than being right"
] |
text-generation | transformers |
#Sherlock DialoGPT Model | {"tags": ["conversational"]} | mdc1616/DialoGPT-large-sherlock | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Sherlock DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained [IndoBERT Base Model (phase1 - uncased)](https://huggingface.co/indobenchmark/indobert-base-p1) model using [Prosa sentiment dataset](https://github.com/indobenchmark/indonlu/tree/master/dataset/... | {} | mdhugol/indonesia-bert-sentiment-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| Indonesian BERT Base Sentiment Classifier is a sentiment-text-classification model. The model was originally the pre-trained IndoBERT Base Model (phase1 - uncased) model using Prosa sentiment dataset
## How to Use
### As Text Classifier
| [
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] |
text-classification | transformers | # German sentiment BERT finetuned on news data
Sentiment analysis model based on https://huggingface.co/oliverguhr/german-sentiment-bert, with additional training on German news texts about migration.
This model is part of the project https://github.com/text-analytics-20/news-sentiment-development, which explores sen... | {} | mdraw/german-news-sentiment-bert | null | [
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"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # German sentiment BERT finetuned on news data
Sentiment analysis model based on URL with additional training on German news texts about migration.
This model is part of the project URL which explores sentiment development in German news articles about migration between 2007 and 2019.
Code for inference (predicting ... | [
"# German sentiment BERT finetuned on news data\n\nSentiment analysis model based on URL with additional training on German news texts about migration.\n\nThis model is part of the project URL which explores sentiment development in German news articles about migration between 2007 and 2019.\n\nCode for inference (... | [
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"# German sentiment BERT finetuned on news data\n\nSentiment analysis model based on URL with additional training on German news texts about migration.\n\nThis model is part of th... |
null | null | # Titanic Disaster
This is titanic model | {} | mecevit/titanic | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| # Titanic Disaster
This is titanic model | [
"# Titanic Disaster\n\nThis is titanic model"
] | [
"TAGS\n#has_space #region-us \n",
"# Titanic Disaster\n\nThis is titanic model"
] |
null | null |
# EfficientTDNN
This repository provides all the necessary tools to perform speaker verification with a NAS alternative, named as EfficientTDNN.
The system can be used to extract speaker embeddings with different model size.
It is trained on Voxceleb2 training data using data augmentation.
The model performance on V... | {"language": ["en"], "license": "mit", "tags": ["embeddings", "Speaker", "Verification", "Identification", "NAS", "TDNN", "pytorch"], "datasets": ["voxceleb1", "voxceleb2"], "metrics": ["EER", {"minDCF": [{"p_target": 0.01}]}]} | mechanicalsea/efficient-tdnn | null | [
"embeddings",
"Speaker",
"Verification",
"Identification",
"NAS",
"TDNN",
"pytorch",
"en",
"dataset:voxceleb1",
"dataset:voxceleb2",
"arxiv:2103.13581",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.13581"
] | [
"en"
] | TAGS
#embeddings #Speaker #Verification #Identification #NAS #TDNN #pytorch #en #dataset-voxceleb1 #dataset-voxceleb2 #arxiv-2103.13581 #license-mit #region-us
| EfficientTDNN
=============
This repository provides all the necessary tools to perform speaker verification with a NAS alternative, named as EfficientTDNN.
The system can be used to extract speaker embeddings with different model size.
It is trained on Voxceleb2 training data using data augmentation.
The model perfo... | [] | [
"TAGS\n#embeddings #Speaker #Verification #Identification #NAS #TDNN #pytorch #en #dataset-voxceleb1 #dataset-voxceleb2 #arxiv-2103.13581 #license-mit #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 565016091
- CO2 Emissions (in grams): 70.54639641012226
## Validation Metrics
- Loss: 0.5170354247093201
- Accuracy: 0.8545909432074056
- Macro F1: 0.7910662503820883
- Micro F1: 0.8545909432074056
- Weighted F1: 0.8539837213761081... | {"language": "fr", "tags": "autonlp", "datasets": ["medA/autonlp-data-FR_another_test"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 70.54639641012226} | medA/autonlp-FR_another_test-565016091 | null | [
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"text-classification",
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"fr",
"dataset:medA/autonlp-data-FR_another_test",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #text-classification #autonlp #fr #dataset-medA/autonlp-data-FR_another_test #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 565016091
- CO2 Emissions (in grams): 70.54639641012226
## Validation Metrics
- Loss: 0.5170354247093201
- Accuracy: 0.8545909432074056
- Macro F1: 0.7910662503820883
- Micro F1: 0.8545909432074056
- Weighted F1: 0.8539837213761081... | [
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"## Validation Metrics\n\n- Loss: 0.5170354247093201\n- Accuracy: 0.8545909432074056\n- Macro F1: 0.7910662503820883\n- Micro F1: 0.8545909432074056\n- Weighted F1: ... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 565016091\n- CO2 Emissions (... |
summarization | transformers |
## BiMeanVAE model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
| {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "inference": false} | megagonlabs/bimeanvae-amzn | null | [
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us
|
## BiMeanVAE model
See original GitHub repo for more details here
| [
"## BiMeanVAE model\nSee original GitHub repo for more details here"
] | [
"TAGS\n#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us \n",
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] |
summarization | transformers |
## BiMeanVAE model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
| {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "inference": false} | megagonlabs/bimeanvae-yelp | null | [
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us
|
## BiMeanVAE model
See original GitHub repo for more details here
| [
"## BiMeanVAE model\nSee original GitHub repo for more details here"
] | [
"TAGS\n#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us \n",
"## BiMeanVAE model\nSee original GitHub repo for more details here"
] |
summarization | transformers |
## Optimus model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
| {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "inference": false} | megagonlabs/optimus-amzn | null | [
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us
|
## Optimus model
See original GitHub repo for more details here
| [
"## Optimus model\nSee original GitHub repo for more details here"
] | [
"TAGS\n#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us \n",
"## Optimus model\nSee original GitHub repo for more details here"
] |
summarization | transformers |
## Optimus model
See original GitHub repo for more details [here](https://github.com/megagonlabs/coop)
| {"language": "en", "license": "bsd-3-clause", "tags": ["summarization"], "inference": false} | megagonlabs/optimus-yelp | null | [
"transformers",
"pytorch",
"summarization",
"en",
"license:bsd-3-clause",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us
|
## Optimus model
See original GitHub repo for more details here
| [
"## Optimus model\nSee original GitHub repo for more details here"
] | [
"TAGS\n#transformers #pytorch #summarization #en #license-bsd-3-clause #region-us \n",
"## Optimus model\nSee original GitHub repo for more details here"
] |
text2text-generation | transformers |
# t5-base-japanese-web-8k (with Byte-fallback, 8K)
## Description
[megagonlabs/t5-base-japanese-web-8k](https://huggingface.co/megagonlabs/t5-base-japanese-web-8k) is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts.
Training codes are [available on GitHub](https://github.com/megago... | {"language": "ja", "license": "apache-2.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["mc4", "wiki40b"]} | megagonlabs/t5-base-japanese-web-8k | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:mc4",
"dataset:wiki40b",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-mc4 #dataset-wiki40b #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-japanese-web-8k (with Byte-fallback, 8K)
## Description
megagonlabs/t5-base-japanese-web-8k is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts.
Training codes are available on GitHub.
The vocabulary size of this model is 8K.
32K version is also available.
### Corpora
W... | [
"# t5-base-japanese-web-8k (with Byte-fallback, 8K)",
"## Description\n\nmegagonlabs/t5-base-japanese-web-8k is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts. \nTraining codes are available on GitHub.\n\nThe vocabulary size of this model is 8K.\n32K version is also available.",... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-mc4 #dataset-wiki40b #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-japanese-web-8k (with Byte-fallback, 8K)",
"## Description\n\nmegagonlabs/t5-... |
text2text-generation | transformers |
# t5-base-japanese-web (with Byte-fallback, 32K)
## Description
[megagonlabs/t5-base-japanese-web](https://huggingface.co/megagonlabs/t5-base-japanese-web) is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts.
Training codes are [available on GitHub](https://github.com/megagonlabs/t5... | {"language": "ja", "license": "apache-2.0", "tags": ["t5", "text2text-generation", "seq2seq"], "datasets": ["mc4", "wiki40b"]} | megagonlabs/t5-base-japanese-web | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"seq2seq",
"ja",
"dataset:mc4",
"dataset:wiki40b",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-mc4 #dataset-wiki40b #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-base-japanese-web (with Byte-fallback, 32K)
## Description
megagonlabs/t5-base-japanese-web is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts.
Training codes are available on GitHub.
The vocabulary size of this model is 32K.
8K version is also available.
### Corpora
We use... | [
"# t5-base-japanese-web (with Byte-fallback, 32K)",
"## Description\n\nmegagonlabs/t5-base-japanese-web is a T5 (Text-to-Text Transfer Transformer) model pre-trained on Japanese web texts. \nTraining codes are available on GitHub.\n\nThe vocabulary size of this model is 32K.\n8K version is also available.",
"#... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #seq2seq #ja #dataset-mc4 #dataset-wiki40b #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-base-japanese-web (with Byte-fallback, 32K)",
"## Description\n\nmegagonlabs/t5-ba... |
null | transformers |
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - [MIYAGINO](https://www.ntj.jac.go.jp/assets/images/member/pertopics/image/per100510_3.jpg)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences.
The input text is toke... | {"language": "ja", "license": "mit", "datasets": ["mC4 Japanese"]} | megagonlabs/transformers-ud-japanese-electra-base-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ja",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"ja"
] | TAGS
#transformers #pytorch #electra #pretraining #ja #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us
|
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - MIYAGINO
This is an ELECTRA model pretrained on approximately 200M Japanese sentences.
The input text is tokenized by SudachiTra with the WordPiece subword tokenizer.
See 'tokenizer_config.json' for the setting details.
## How to use
... | [
"# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese) - MIYAGINO\n\nThis is an ELECTRA model pretrained on approximately 200M Japanese sentences.\n\nThe input text is tokenized by SudachiTra with the WordPiece subword tokenizer.\nSee 'tokenizer_config.json' for the setting details.",
"## ... | [
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feature-extraction | transformers |
# transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/... | {"language": ["ja"], "license": "mit", "tags": ["PyTorch", "Transformers", "spaCy", "ELECTRA", "GiNZA", "mC4", "UD_Japanese-BCCWJ", "GSK2014-A", "ja", "MIT"], "datasets": ["mC4", "UD_Japanese_BCCWJ r2.8", "GSK2014-A(2019)"], "metrics": ["UAS", "LAS", "UPOS"], "thumbnail": "https://raw.githubusercontent.com/megagonlabs/... | megagonlabs/transformers-ud-japanese-electra-base-ginza-510 | null | [
"transformers",
"pytorch",
"electra",
"feature-extraction",
"PyTorch",
"Transformers",
"spaCy",
"ELECTRA",
"GiNZA",
"mC4",
"UD_Japanese-BCCWJ",
"GSK2014-A",
"ja",
"MIT",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"ja"
] | TAGS
#transformers #pytorch #electra #feature-extraction #PyTorch #Transformers #spaCy #ELECTRA #GiNZA #mC4 #UD_Japanese-BCCWJ #GSK2014-A #ja #MIT #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us
|
# transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese)
This is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD\_Japanese\_BCCWJ r2.8.
The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-dis... | [
"# transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese)\n\nThis is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD\\_Japanese\\_BCCWJ r2.8.\n\nThe base pretrain model is megagonlabs/transformers-ud-japanese-electr... | [
"TAGS\n#transformers #pytorch #electra #feature-extraction #PyTorch #Transformers #spaCy #ELECTRA #GiNZA #mC4 #UD_Japanese-BCCWJ #GSK2014-A #ja #MIT #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us \n",
"# transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese)\n\nThis is an... |
null | transformers |
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese)
This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3) ... | {"language": "ja", "license": "mit", "datasets": ["mC4 Japanese"]} | megagonlabs/transformers-ud-japanese-electra-base-ginza | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ja",
"arxiv:1910.10683",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [
"ja"
] | TAGS
#transformers #pytorch #electra #pretraining #ja #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us
|
# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese)
This is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD\_Japanese\_BCCWJ r2.8.
The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrim... | [
"# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese)\n\nThis is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD\\_Japanese\\_BCCWJ r2.8.\n\nThe base pretrain model is megagonlabs/transformers-ud-japanese-electra-ba... | [
"TAGS\n#transformers #pytorch #electra #pretraining #ja #arxiv-1910.10683 #license-mit #endpoints_compatible #region-us \n",
"# transformers-ud-japanese-electra-ginza (sudachitra-wordpiece, mC4 Japanese)\n\nThis is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and fin... |
token-classification | flair | # Arabic NER Model for AQMAR dataset
Training was conducted over 86 epochs, using a linear decaying learning rate of 2e-05, starting from 0.3 and a batch size of 48 with fastText and Flair forward and backward embeddings.
## Original Dataset:
- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
## Results:
- F1-score (m... | {"language": "ar", "license": "apache-2.0", "tags": ["flair", "Text Classification", "token-classification", "sequence-tagger-model"], "datasets": ["AQMAR", "ANERcorp"], "metrics": ["f1"], "thumbnail": "https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb/@@image... | megantosh/flair-arabic-MSA-aqmar | null | [
"flair",
"pytorch",
"Text Classification",
"token-classification",
"sequence-tagger-model",
"ar",
"dataset:AQMAR",
"dataset:ANERcorp",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#flair #pytorch #Text Classification #token-classification #sequence-tagger-model #ar #dataset-AQMAR #dataset-ANERcorp #license-apache-2.0 #region-us
| Arabic NER Model for AQMAR dataset
==================================
Training was conducted over 86 epochs, using a linear decaying learning rate of 2e-05, starting from 0.3 and a batch size of 48 with fastText and Flair forward and backward embeddings.
Original Dataset:
-----------------
* AQMAR
Results:
----... | [] | [
"TAGS\n#flair #pytorch #Text Classification #token-classification #sequence-tagger-model #ar #dataset-AQMAR #dataset-ANERcorp #license-apache-2.0 #region-us \n"
] |
token-classification | flair |
# Arabic Flair + fastText Part-of-Speech tagging Model (Egyptian and Levant)
Pretrained Part-of-Speech tagging model built on a joint corpus written in Egyptian and Levantine (Jordanian, Lebanese, Palestinian, Syrian) dialects with code-switching of Egyptian Arabic and English. The model is trained using [Flair](http... | {"language": ["ar", "en"], "license": "apache-2.0", "tags": ["flair", "token-classification", "sequence-tagger-model", "Dialectal Arabic", "Code-Switching", "Code-Mixing"], "datasets": ["4Dialects", "MADAR", "CSCS"], "metrics": ["f1"], "thumbnail": "https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/reso... | megantosh/flair-arabic-dialects-codeswitch-egy-lev | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"Dialectal Arabic",
"Code-Switching",
"Code-Mixing",
"ar",
"en",
"dataset:4Dialects",
"dataset:MADAR",
"dataset:CSCS",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar",
"en"
] | TAGS
#flair #pytorch #token-classification #sequence-tagger-model #Dialectal Arabic #Code-Switching #Code-Mixing #ar #en #dataset-4Dialects #dataset-MADAR #dataset-CSCS #license-apache-2.0 #region-us
| Arabic Flair + fastText Part-of-Speech tagging Model (Egyptian and Levant)
==========================================================================
Pretrained Part-of-Speech tagging model built on a joint corpus written in Egyptian and Levantine (Jordanian, Lebanese, Palestinian, Syrian) dialects with code-switchin... | [] | [
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #Dialectal Arabic #Code-Switching #Code-Mixing #ar #en #dataset-4Dialects #dataset-MADAR #dataset-CSCS #license-apache-2.0 #region-us \n"
] |
token-classification | flair | # Arabic NER Model using Flair Embeddings
Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, starting from 0.225 and a batch size of 32 with GloVe and Flair forward and backward embeddings.
## Original Datasets:
- [AQMAR](http://www.cs.cmu.edu/~ark/ArabicNER/)
- [ANERcorp](http://c... | {"language": ["ar", "en"], "license": "apache-2.0", "tags": ["flair", "Text Classification", "token-classification", "sequence-tagger-model"], "datasets": ["AQMAR", "ANERcorp"], "metrics": ["f1"], "thumbnail": "https://www.informatik.hu-berlin.de/en/forschung-en/gebiete/ml-en/resolveuid/a6f82e0d7fa446a59c902cac4cafa9cb... | megantosh/flair-arabic-multi-ner | null | [
"flair",
"pytorch",
"Text Classification",
"token-classification",
"sequence-tagger-model",
"ar",
"en",
"dataset:AQMAR",
"dataset:ANERcorp",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar",
"en"
] | TAGS
#flair #pytorch #Text Classification #token-classification #sequence-tagger-model #ar #en #dataset-AQMAR #dataset-ANERcorp #license-apache-2.0 #has_space #region-us
| Arabic NER Model using Flair Embeddings
=======================================
Training was conducted over 94 epochs, using a linear decaying learning rate of 2e-05, starting from 0.225 and a batch size of 32 with GloVe and Flair forward and backward embeddings.
Original Datasets:
------------------
* AQMAR
* AN... | [] | [
"TAGS\n#flair #pytorch #Text Classification #token-classification #sequence-tagger-model #ar #en #dataset-AQMAR #dataset-ANERcorp #license-apache-2.0 #has_space #region-us \n"
] |
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. -->
# hitalm-xlmroberta-finetuned
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "hitalm-xlmroberta-finetuned", "results": []}]} | meghana/hitalm-xlmroberta-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| hitalm-xlmroberta-finetuned
===========================
This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.7745
Model description
-----------------
More information needed
Intended uses & limitations
-----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #xlm-roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
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. -->
# hitalmqa-finetuned-squad
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "hitalmqa-finetuned-squad", "results": []}]} | meghana/hitalmqa-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
|
# hitalmqa-finetuned-squad
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# hitalmqa-finetuned-squad\n\nThis model is a fine-tuned version of xlm-roberta-large on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"# hitalmqa-finetuned-squad\n\nThis model is a fine-tuned version of xlm-roberta-large on an unknown dataset.",
"## Model description\n\nMore information needed"... |
text-generation | transformers |
# Melon Bot DialoGPT Model | {"tags": ["conversational"]} | melon422/DialoGPT-medium-MelonBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Melon Bot DialoGPT Model | [
"# Melon Bot DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Melon Bot DialoGPT Model"
] |
text-generation | transformers |
# Melon Bot2 DialoGPT Model | {"tags": ["conversational"]} | melon422/DialoGPT-medium-MelonBot2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Melon Bot2 DialoGPT Model | [
"# Melon Bot2 DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Melon Bot2 DialoGPT Model"
] |
token-classification | flair | ## Test model README
Some test README description | {"tags": ["flair", "token-classification"], "widget": [{"text": "does this work"}]} | menciusds/flairmodel | null | [
"flair",
"pytorch",
"token-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#flair #pytorch #token-classification #region-us
| ## Test model README
Some test README description | [
"## Test model README\nSome test README description"
] | [
"TAGS\n#flair #pytorch #token-classification #region-us \n",
"## Test model README\nSome test README description"
] |
fill-mask | transformers | # MentalBERT
[MentalBERT](https://arxiv.org/abs/2110.15621) is a model initialized with BERT-Base (`uncased_L-12_H-768_A-12`) and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with [Huggingface’s Transformers library](https://github.c... | {"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mental health"]} | mental/mental-bert-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"mental health",
"en",
"arxiv:2110.15621",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.15621"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #mental health #en #arxiv-2110.15621 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # MentalBERT
MentalBERT is a model initialized with BERT-Base ('uncased_L-12_H-768_A-12') and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with Huggingface’s Transformers library.
We use four Nvidia Tesla v100 GPUs to train the two ... | [
"# MentalBERT\n\nMentalBERT is a model initialized with BERT-Base ('uncased_L-12_H-768_A-12') and trained with mental health-related posts collected from Reddit. \n\nWe follow the standard pretraining protocols of BERT and RoBERTa with Huggingface’s Transformers library.\n\nWe use four Nvidia Tesla v100 GPUs to tra... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #mental health #en #arxiv-2110.15621 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MentalBERT\n\nMentalBERT is a model initialized with BERT-Base ('uncased_L-12_H-768_A-12') and trained with mental health-related post... |
fill-mask | transformers |
# MentalRoBERTa
[MentalRoBERTa](https://arxiv.org/abs/2110.15621) is a model initialized with RoBERTa-Base (`cased_L-12_H-768_A-12`) and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with [Huggingface’s Transformers library](https://... | {"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mental health"]} | mental/mental-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"mental health",
"en",
"arxiv:2110.15621",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.15621"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #mental health #en #arxiv-2110.15621 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# MentalRoBERTa
MentalRoBERTa is a model initialized with RoBERTa-Base ('cased_L-12_H-768_A-12') and trained with mental health-related posts collected from Reddit.
We follow the standard pretraining protocols of BERT and RoBERTa with Huggingface’s Transformers library.
We use four Nvidia Tesla v100 GPUs to train ... | [
"# MentalRoBERTa\n\nMentalRoBERTa is a model initialized with RoBERTa-Base ('cased_L-12_H-768_A-12') and trained with mental health-related posts collected from Reddit. \n\nWe follow the standard pretraining protocols of BERT and RoBERTa with Huggingface’s Transformers library.\n\nWe use four Nvidia Tesla v100 GPUs... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #mental health #en #arxiv-2110.15621 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# MentalRoBERTa\n\nMentalRoBERTa is a model initialized with RoBERTa-Base ('cased_L-12_H-768_A-12') and trained with mental health-re... |
null | transformers |
An ELECTRA-small model for Ancient Greek, trained on texts from Homer up until the 4th century AD. | {"language": ["grc"], "tags": ["ELECTRA", "TensorFlow"]} | mercelisw/electra-grc | null | [
"transformers",
"pytorch",
"ELECTRA",
"TensorFlow",
"grc",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"grc"
] | TAGS
#transformers #pytorch #ELECTRA #TensorFlow #grc #endpoints_compatible #region-us
|
An ELECTRA-small model for Ancient Greek, trained on texts from Homer up until the 4th century AD. | [] | [
"TAGS\n#transformers #pytorch #ELECTRA #TensorFlow #grc #endpoints_compatible #region-us \n"
] |
null | keras | # TODO: Fill this model card
| {"tags": ["Keras"]} | merve/conv-autoencoder | null | [
"keras",
"Keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #Keras #region-us
| # TODO: Fill this model card
| [
"# TODO: Fill this model card"
] | [
"TAGS\n#keras #Keras #region-us \n",
"# TODO: Fill this model card"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# merve/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["conll2003"], "model-index": [{"name": "merve/distilbert-base-uncased-finetuned-ner", "results": []}]} | merve/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| merve/distilbert-base-uncased-finetuned-ner
===========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2037
* Validation Loss: 0.0703
* Epoch: 0
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 2631, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #token-classification #generated_from_keras_callback #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'... |
object-detection | keras |
## Model description
This model has couple of Dense layers.
## Intended uses & limitations
It's intended to demonstrate capabilities of Hub for Keras on my blog post!
## Training and evaluation data
It's trained on dummy data.
Above information is filled manually.
## Training procedure
### Training hyperparam... | {"library_name": "keras", "tags": ["object-detection"]} | merve/model-card-example | null | [
"keras",
"tensorboard",
"object-detection",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #tensorboard #object-detection #region-us
| Model description
-----------------
This model has couple of Dense layers.
Intended uses & limitations
---------------------------
It's intended to demonstrate capabilities of Hub for Keras on my blog post!
Training and evaluation data
----------------------------
It's trained on dummy data.
Above informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32\n\n\nTraining Metrics\n----------------\n\n\... | [
"TAGS\n#keras #tensorboard #object-detection #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': 0.001, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_... |
text-classification | transformers |
# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question ... | {} | mervenoyan/PubMedBERT-QNLI | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task
Use case: You can use it to search through a document for a given question, to see if your question is answered in that document.
LABEL0 is "not entailment" meaning your question is not answered by the context and LABEL1 is "entailment" meaning your question ... | [
"# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task\n\nUse case: You can use it to search through a document for a given question, to see if your question is answered in that document.\n\nLABEL0 is \"not entailment\" meaning your question is not answered by the context and LABEL1 is \"entailment\" meaning yo... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# PubMedBERT Abstract + Full Text Fine-Tuned on QNLI Task\n\nUse case: You can use it to search through a document for a given question, to see if your question is answered in that document.\n\nLA... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | mewmew/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
question-answering | transformers |
# Model Card for Model ID
Albert XXLarge V2 model, fine-tuned for SQuAD V2
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details... | {} | mfeb/albert-xxlarge-v2-squad2 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #pytorch #albert #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
Albert XXLarge V2 model, fine-tuned for SQuAD V2
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## Model Details
### Model Description
- Developed by: Mark Feblowitz, IBM Research
- Shared by [optional]:
- Model type:
- Lang... | [
"# Model Card for Model ID\n\nAlbert XXLarge V2 model, fine-tuned for SQuAD V2\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: Mark Feblowitz, IBM Research\n- Shared by [optional]:... | [
"TAGS\n#transformers #pytorch #albert #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID\n\nAlbert XXLarge V2 model, fine-tuned for SQuAD V2\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model... |
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... | mflorinsky/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+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: 0.8753
* Matthews Correlation: 0.5226
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... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | null | Hello World
Test from Colab
| {} | mfuntowicz/test-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Hello World
Test from Colab
| [] | [
"TAGS\n#region-us \n"
] |
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. -->
# bertlet-base-uncased-for-sequence-classification
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "bertlet-base-uncased-for-sequence-classification", "results": []}]} | mgreenbe/bertlet-base-uncased-for-sequence-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# bertlet-base-uncased-for-sequence-classification
This model is a fine-tuned version of [](URL on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Trainin... | [
"# bertlet-base-uncased-for-sequence-classification\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Train... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# bertlet-base-uncased-for-sequence-classification\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.",
"## Model description\n\nMore information need... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 5521155
## Validation Metrics
- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro P... | {"language": "it", "tags": ["autonlp"], "datasets": ["mgrella/autonlp-data-bank-transaction-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | mgrella/autonlp-bank-transaction-classification-5521155 | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autonlp",
"it",
"dataset:mgrella/autonlp-data-bank-transaction-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #autonlp #it #dataset-mgrella/autonlp-data-bank-transaction-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 5521155
## Validation Metrics
- Loss: 1.3173143863677979
- Accuracy: 0.8220706757594545
- Macro F1: 0.5713688384455807
- Micro F1: 0.8220706757594544
- Weighted F1: 0.8217158913702755
- Macro Precision: 0.6064387992817253
- Micro P... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 5521155",
"## Validation Metrics\n\n- Loss: 1.3173143863677979\n- Accuracy: 0.8220706757594545\n- Macro F1: 0.5713688384455807\n- Micro F1: 0.8220706757594544\n- Weighted F1: 0.8217158913702755\n- Macro Precision: 0.60643879... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #autonlp #it #dataset-mgrella/autonlp-data-bank-transaction-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 5521155"... |
audio-to-audio | asteroid |
## Asteroid model `mhu-coder/ConvTasNet_Libri1Mix_enhsingle`
Imported from [Zenodo](https://zenodo.org/record/4301955#.X9cj98Jw0bY)
### Description:
This model was trained by Mathieu Hu using the librimix/ConvTasNet recipe in
[Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` ta... | {"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["libri1mix", "enh_single"]} | mhu-coder/ConvTasNet_Libri1Mix_enhsingle | null | [
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:libri1mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-libri1mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'mhu-coder/ConvTasNet_Libri1Mix_enhsingle'
Imported from Zenodo
### Description:
This model was trained by Mathieu Hu using the librimix/ConvTasNet recipe in
Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
### Training config:
### Results:
### License notice:
This w... | [
"## Asteroid model 'mhu-coder/ConvTasNet_Libri1Mix_enhsingle'\nImported from Zenodo",
"### Description:\nThis model was trained by Mathieu Hu using the librimix/ConvTasNet recipe in\nAsteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.",
"### Training config:",
"### Results:",
"### L... | [
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-libri1mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'mhu-coder/ConvTasNet_Libri1Mix_enhsingle'\nImported from Zenodo",
"### Description:\nThis model was trained by Mathieu Hu using the librimix/ConvTasNet re... |
fill-mask | transformers | for contest
| {} | miaomiaomiao/macbert_ngram_miao | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| for contest
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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