modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-12 06:28:00
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 517
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-07-12 06:24:43
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
sd-concepts-library/kysa-v-style | sd-concepts-library | 2022-09-24T19:02:40Z | 0 | 2 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-24T19:02:27Z | ---
license: mit
---
### kysa-v-style on Stable Diffusion
This is the `<kysa-v-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
kk00165/Jiang | kk00165 | 2022-09-24T18:48:44Z | 0 | 0 | null | [
"region:us"
]
| null | 2022-09-24T18:39:49Z | Jiangstyle on Stable Diffusion
This is the <Jiangstyle> concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the Stable Conceptualizer notebook. You can also train your own concepts and load them into the concept libraries using this notebook.
Here is the new concept you will be able to use as a style: |
bintualkassoum/shopinspo_demo | bintualkassoum | 2022-09-24T18:26:24Z | 237 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2022-09-24T18:26:10Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: shopinspo_demo
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5267857313156128
---
# shopinspo_demo
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### womens dress
#### womens pants
#### womens shorts
#### womens skirt
#### womens top
 |
kevinbram/testarbara | kevinbram | 2022-09-24T18:16:20Z | 61 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2022-09-24T17:37:28Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: kevinbram/testarbara
results: []
---
<!-- 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. -->
# kevinbram/testarbara
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.4900
- Train End Logits Accuracy: 0.6129
- Train Start Logits Accuracy: 0.5735
- Validation Loss: 1.1335
- Validation End Logits Accuracy: 0.6908
- Validation Start Logits Accuracy: 0.6545
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11064, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.4900 | 0.6129 | 0.5735 | 1.1335 | 0.6908 | 0.6545 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
rram12/pixelcopter | rram12 | 2022-09-24T18:13:16Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2022-09-24T18:13:09Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.80 +/- 13.74
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
edumunozsala/bertin-sts-cc-news-es | edumunozsala | 2022-09-24T18:02:46Z | 4 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"dataset:LeoCordoba/CC-NEWS-ES-titles",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2022-08-22T13:51:47Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- LeoCordoba/CC-NEWS-ES-titles
---
# bertin-sts-cc-news-es
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 becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('edumunozsala/bertin-sts-cc-news-es')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('edumunozsala/bertin-sts-cc-news-es')
model = AutoModel.from_pretrained('edumunozsala/bertin-sts-cc-news-es')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=bertin-sts-cc-news-es)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 13054 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3916,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
azaidi-face/xlm-roberta-base-finetuned-panx-de | azaidi-face | 2022-09-24T17:16:43Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2022-09-24T17:09:45Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8663101604278075
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8663
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2581 | 1.0 | 525 | 0.1690 | 0.8303 |
| 0.1305 | 2.0 | 1050 | 0.1352 | 0.8484 |
| 0.0839 | 3.0 | 1575 | 0.1339 | 0.8663 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
robingeibel/led-large-16384-finetuned-big_patent | robingeibel | 2022-09-24T16:03:38Z | 93 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"led",
"feature-extraction",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2022-06-28T10:32:30Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: led-large-16384-finetuned-big_patent
results: []
---
<!-- 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. -->
# led-large-16384-finetuned-big_patent
This model is a fine-tuned version of [robingeibel/led-large-16384-finetuned-big_patent](https://huggingface.co/robingeibel/led-large-16384-finetuned-big_patent) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.22.1
- TensorFlow 2.8.2
- Datasets 2.5.1
- Tokenizers 0.12.1
|
sd-concepts-library/lex | sd-concepts-library | 2022-09-24T15:55:39Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2022-09-24T15:55:33Z | ---
license: mit
---
### lex on Stable Diffusion
This is the `<lex>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
truongpdd/vi-en-roberta-base | truongpdd | 2022-09-24T15:17:38Z | 37 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2022-09-19T02:44:51Z | ```
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('truongpdd/vi-en-roberta-base')
model = AutoModel.from_pretrained('truongpdd/vi-en-roberta-base', from_flax=True)
``` |
masakhane/afrimbart_bam_fr_news | masakhane | 2022-09-24T15:08:14Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:31:01Z | ---
license: afl-3.0
language:
- bam
- fr
---
|
masakhane/afrimt5_bam_fr_news | masakhane | 2022-09-24T15:08:13Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:32:00Z | ---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/afribyt5_bam_fr_news | masakhane | 2022-09-24T15:08:11Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:36:44Z | ---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/mt5_bam_fr_news | masakhane | 2022-09-24T15:08:08Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:50:43Z | ---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/mbart50_fr_bam_news | masakhane | 2022-09-24T15:08:07Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:50:10Z | ---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_news | masakhane | 2022-09-24T15:08:05Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:45:42Z | ---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_bam_fr_rel_news | masakhane | 2022-09-24T15:08:05Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:42:59Z | ---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_bam_fr_rel_news_ft | masakhane | 2022-09-24T15:08:04Z | 107 | 1 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:45:01Z | ---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel_news_ft | masakhane | 2022-09-24T15:08:03Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:44:33Z | ---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel_ft | masakhane | 2022-09-24T15:08:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:48:57Z | ---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bam_rel | masakhane | 2022-09-24T15:08:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bam",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:47:29Z | ---
language:
- fr
- bam
license: afl-3.0
---
|
masakhane/m2m100_418M_bam_fr_rel_ft | masakhane | 2022-09-24T15:08:01Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"bam",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-09T17:49:26Z | ---
language:
- bam
- fr
license: afl-3.0
---
|
masakhane/afrimt5_fr_bbj_news | masakhane | 2022-09-24T15:08:01Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"bbj",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:50:08Z | ---
language:
- fr
- bbj
license: afl-3.0
---
|
masakhane/afrimt5_bbj_fr_news | masakhane | 2022-09-24T15:08:00Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:51:01Z | ---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/byt5_fr_bbj_news | masakhane | 2022-09-24T15:07:57Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"fr",
"bbj",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:53:24Z | ---
language:
- fr
- bbj
license: afl-3.0
---
|
masakhane/byt5_bbj_fr_news | masakhane | 2022-09-24T15:07:56Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:53:45Z | ---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/mbart50_fr_bbj_news | masakhane | 2022-09-24T15:07:55Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fr",
"bbj",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:54:40Z | ---
language:
- fr
- bbj
license: afl-3.0
---
|
masakhane/mt5_bbj_fr_news | masakhane | 2022-09-24T15:07:55Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:54:00Z | ---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_bbj_fr_news | masakhane | 2022-09-24T15:07:54Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:55:52Z | ---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/mbart50_bbj_fr_news | masakhane | 2022-09-24T15:07:54Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:55:00Z | ---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_bbj_news | masakhane | 2022-09-24T15:07:53Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"bbj",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:56:11Z | ---
language:
- fr
- bbj
license: afl-3.0
---
|
masakhane/m2m100_418M_bbj_fr_rel_news | masakhane | 2022-09-24T15:07:52Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"bbj",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-13T16:56:58Z | ---
language:
- bbj
- fr
license: afl-3.0
---
|
masakhane/afrimt5_ewe_fr_news | masakhane | 2022-09-24T15:07:48Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:33:54Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/afrimbart_ewe_fr_news | masakhane | 2022-09-24T15:07:48Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:34:14Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/afribyt5_fr_ewe_news | masakhane | 2022-09-24T15:07:47Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"fr",
"ewe",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:35:56Z | ---
language:
- fr
- ewe
license: afl-3.0
---
|
masakhane/byt5_fr_ewe_news | masakhane | 2022-09-24T15:07:46Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"fr",
"ewe",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:36:31Z | ---
language:
- fr
- ewe
license: afl-3.0
---
|
masakhane/mbart50_ewe_fr_news | masakhane | 2022-09-24T15:07:44Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:39:03Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/mt5_fr_ewe_news | masakhane | 2022-09-24T15:07:43Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"ewe",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:38:01Z | ---
language:
- fr
- ewe
license: afl-3.0
---
|
masakhane/m2m100_418M_ewe_fr_news | masakhane | 2022-09-24T15:07:42Z | 102 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:40:02Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_ewe_fr_rel_news | masakhane | 2022-09-24T15:07:41Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:42:16Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_ewe_rel_news | masakhane | 2022-09-24T15:07:41Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"ewe",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:41:54Z | ---
language:
- fr
- ewe
license: afl-3.0
---
|
masakhane/m2m100_418M_ewe_fr_rel_news_ft | masakhane | 2022-09-24T15:07:40Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:43:06Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_ewe_rel_news_ft | masakhane | 2022-09-24T15:07:39Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"ewe",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:42:48Z | ---
language:
- fr
- ewe
license: afl-3.0
---
|
masakhane/m2m100_418M_ewe_fr_rel | masakhane | 2022-09-24T15:07:38Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ewe",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-15T08:44:39Z | ---
language:
- ewe
- fr
license: afl-3.0
---
|
masakhane/afrimt5_fr_fon_news | masakhane | 2022-09-24T15:07:37Z | 103 | 1 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"fon",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:18:35Z | ---
language:
- fr
- fon
license: afl-3.0
---
|
masakhane/mt5_fon_fr_news | masakhane | 2022-09-24T15:07:33Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fon",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:33:53Z | ---
language:
- fon
- fr
license: afl-3.0
---
|
masakhane/afrimbart_fon_fr_news | masakhane | 2022-09-24T15:07:33Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fon",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:32:01Z | ---
language:
- fon
- fr
license: afl-3.0
---
|
masakhane/byt5_fon_fr_news | masakhane | 2022-09-24T15:07:31Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"fon",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:33:35Z | ---
language:
- fon
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fon_fr_news | masakhane | 2022-09-24T15:07:31Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fon",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:35:38Z | ---
language:
- fon
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_fon_news | masakhane | 2022-09-24T15:07:30Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"fon",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:35:22Z | ---
language:
- fr
- fon
license: afl-3.0
---
|
masakhane/m2m100_418M_fon_fr_rel_ft | masakhane | 2022-09-24T15:07:29Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fon",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:37:42Z | ---
language:
- fon
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_fon_rel_ft | masakhane | 2022-09-24T15:07:28Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"fon",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:37:26Z | ---
language:
- fr
- fon
license: afl-3.0
---
|
masakhane/m2m100_418M_fon_fr_rel | masakhane | 2022-09-24T15:07:27Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fon",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T12:37:58Z | ---
language:
- fon
- fr
license: afl-3.0
---
|
masakhane/afrimbart_fr_mos_news | masakhane | 2022-09-24T15:07:25Z | 115 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T20:24:16Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/afrimbart_mos_fr_news | masakhane | 2022-09-24T15:07:25Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"mos",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T20:24:34Z | ---
language:
- mos
- fr
license: afl-3.0
---
|
masakhane/mt5_fr_mos_news | masakhane | 2022-09-24T15:07:24Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T20:26:22Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/byt5_fr_mos_news | masakhane | 2022-09-24T15:07:22Z | 97 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T20:27:26Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/afrimt5_mos_fr_news | masakhane | 2022-09-24T15:07:20Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"mos",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T21:42:25Z | ---
language:
- mos
- fr
license: afl-3.0
---
|
masakhane/afrimt5_fr_mos_news | masakhane | 2022-09-24T15:07:19Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-16T21:42:03Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/m2m100_418M_mos_fr_news | masakhane | 2022-09-24T15:07:18Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"mos",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T08:06:29Z | ---
language:
- mos
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_mos_rel_news_ft | masakhane | 2022-09-24T15:07:18Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T08:07:16Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_mos_rel | masakhane | 2022-09-24T15:07:17Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T10:47:34Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/m2m100_418M_mos_fr_rel | masakhane | 2022-09-24T15:07:16Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"mos",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T10:47:15Z | ---
language:
- mos
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_mos_rel_news | masakhane | 2022-09-24T15:07:16Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"mos",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T11:39:47Z | ---
language:
- fr
- mos
license: afl-3.0
---
|
masakhane/m2m100_418M_mos_fr_rel_news_ft | masakhane | 2022-09-24T15:07:15Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"mos",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T08:07:30Z | ---
language:
- mos
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_mos_fr_rel_ft | masakhane | 2022-09-24T15:07:14Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"mos",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-17T10:46:39Z | ---
language:
- mos
- fr
license: afl-3.0
---
|
masakhane/afrimt5_fr_wol_news | masakhane | 2022-09-24T15:07:12Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"fr",
"wol",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:09:02Z | ---
language:
- fr
- wol
license: afl-3.0
---
|
masakhane/mbart50_fr_wol_news | masakhane | 2022-09-24T15:07:09Z | 121 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"fr",
"wol",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:11:34Z | ---
language:
- fr
- wol
license: afl-3.0
---
|
masakhane/mbart50_wol_fr_news | masakhane | 2022-09-24T15:07:08Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"wol",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:11:20Z | ---
language:
- wol
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_wol_fr_rel_news_ft | masakhane | 2022-09-24T15:07:05Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"wol",
"fr",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:14:13Z | ---
language:
- wol
- fr
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_rel_news | masakhane | 2022-09-24T15:07:05Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"wol",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:13:43Z | ---
language:
- fr
- wol
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_rel | masakhane | 2022-09-24T15:07:03Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"wol",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:15:21Z | ---
language:
- fr
- wol
license: afl-3.0
---
|
masakhane/m2m100_418M_fr_wol_rel_news_ft | masakhane | 2022-09-24T15:07:03Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"fr",
"wol",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-20T12:13:57Z | ---
language:
- fr
- wol
license: afl-3.0
---
|
masakhane/afrimbart_en_ibo_news | masakhane | 2022-09-24T15:07:02Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"ibo",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:42:12Z | ---
language:
- en
- ibo
license: afl-3.0
---
|
masakhane/mt5_ibo_en_news | masakhane | 2022-09-24T15:06:57Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"ibo",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:44:34Z | ---
language:
- ibo
- en
license: afl-3.0
---
|
masakhane/mt5_en_ibo_news | masakhane | 2022-09-24T15:06:56Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"en",
"ibo",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:44:51Z | ---
language:
- en
- ibo
license: afl-3.0
---
|
masakhane/m2m100_418M_en_ibo_rel_news | masakhane | 2022-09-24T15:06:55Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"ibo",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:47:44Z | ---
language:
- en
- ibo
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_news | masakhane | 2022-09-24T15:06:54Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ibo",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:47:05Z | ---
language:
- ibo
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_rel_ft | masakhane | 2022-09-24T15:06:53Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ibo",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:48:30Z | ---
language:
- ibo
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_rel_news_ft | masakhane | 2022-09-24T15:06:53Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ibo",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:48:13Z | ---
language:
- ibo
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_ibo_rel_news_ft | masakhane | 2022-09-24T15:06:52Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"ibo",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:47:57Z | ---
language:
- en
- ibo
license: afl-3.0
---
|
masakhane/m2m100_418M_ibo_en_rel | masakhane | 2022-09-24T15:06:51Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"ibo",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-04-21T20:49:17Z | ---
language:
- ibo
- en
license: afl-3.0
---
|
masakhane/afrimbart_hau_en_news | masakhane | 2022-09-24T15:06:48Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"hau",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:46:35Z | ---
language:
- hau
- en
license: afl-3.0
---
|
masakhane/afribyt5_en_hau_news | masakhane | 2022-09-24T15:06:48Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"hau",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:47:40Z | ---
language:
- en
- hau
license: afl-3.0
---
|
masakhane/afribyt5_hau_en_news | masakhane | 2022-09-24T15:06:47Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"hau",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:47:56Z | ---
language:
- hau
- en
license: afl-3.0
---
|
masakhane/mt5_en_hau_news | masakhane | 2022-09-24T15:06:46Z | 102 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"en",
"hau",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:48:51Z | ---
language:
- en
- hau
license: afl-3.0
---
|
masakhane/mbart50_hau_en_news | masakhane | 2022-09-24T15:06:45Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"hau",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:49:35Z | ---
language:
- hau
- en
license: afl-3.0
---
|
masakhane/mt5_hau_en_news | masakhane | 2022-09-24T15:06:45Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"hau",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:49:10Z | ---
language:
- hau
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_hau_rel_news_ft | masakhane | 2022-09-24T15:06:42Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"hau",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:52:07Z | ---
language:
- en
- hau
license: afl-3.0
---
|
masakhane/m2m100_418M_en_hau_rel | masakhane | 2022-09-24T15:06:39Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"hau",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-03T12:53:19Z | ---
language:
- en
- hau
license: afl-3.0
---
|
masakhane/afrimbart_en_lug_news | masakhane | 2022-09-24T15:06:36Z | 123 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:07:05Z | ---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/afribyt5_lug_en_news | masakhane | 2022-09-24T15:06:35Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:11:22Z | ---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/byt5_en_lug_news | masakhane | 2022-09-24T15:06:34Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:12:52Z | ---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_news | masakhane | 2022-09-24T15:06:31Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:19:41Z | ---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel_news | masakhane | 2022-09-24T15:06:30Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:20:27Z | ---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_rel_news_ft | masakhane | 2022-09-24T15:06:28Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:21:04Z | ---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_en_lug_rel_ft | masakhane | 2022-09-24T15:06:27Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"lug",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:21:45Z | ---
language:
- en
- lug
license: afl-3.0
---
|
masakhane/m2m100_418M_lug_en_rel_ft | masakhane | 2022-09-24T15:06:26Z | 99 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"lug",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-05T11:22:09Z | ---
language:
- lug
- en
license: afl-3.0
---
|
masakhane/afrimbart_pcm_en_news | masakhane | 2022-09-24T15:06:24Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"pcm",
"en",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-09T13:04:55Z | ---
language:
- pcm
- en
license: afl-3.0
---
|
masakhane/byt5_en_pcm_news | masakhane | 2022-09-24T15:06:20Z | 111 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"pcm",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2022-05-10T06:41:46Z | ---
language:
- en
- pcm
license: afl-3.0
---
|
Subsets and Splits