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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. -->
# abnv15/MLMA
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "abnv15/MLMA", "results": []}]} | abnv15/MLMA | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T18:26:31+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| abnv15/MLMA
===========
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0237
* Validation Loss: 0.0647
* Epoch: 2
Model description
-----------------
More information needed
Intended uses & limitations
------... | [
"### 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': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_... |
token-classification | transformers |
# BERT BASE (cased) finetuned on Bulgarian named-entity-recognition data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This m... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/bert-base-ner-theseus-bg | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:2002.02925",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-16T18:31:59+00:00 | [
"1810.04805",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #bert #token-classification #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us
|
# BERT BASE (cased) finetuned on Bulgarian named-entity-recognition data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it does make a difference
between bulgarian and Bulgarian. The t... | [
"# BERT BASE (cased) finetuned on Bulgarian named-entity-recognition data\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it does make a difference\nbetween bulgarian and Bulgari... | [
"TAGS\n#transformers #pytorch #bert #token-classification #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us \n",
"# BERT BASE (cased) finetuned on Bulgarian named-entity-recognition data\n\nPretrained model on Bulgarian... |
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. -->
# anarise1/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "anarise1/bert-finetuned-ner", "results": []}]} | anarise1/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T18:33:36+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| anarise1/bert-finetuned-ner
===========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0242
* Validation Loss: 0.0558
* Epoch: 2
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'inner\\_optimizer': {'class\\_name': '... |
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. -->
# lideming7757/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lideming7757/bert-finetuned-ner", "results": []}]} | lideming7757/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T18:49:47+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| lideming7757/bert-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0214
* Validation Loss: 0.0636
* Epoch: 2
Model description
-----------------
More information nee... | [
"### 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': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
null | null | I like star
| {} | Youxiu/CYnic | null | [
"region:us"
] | null | 2022-04-16T18:53:12+00:00 | [] | [] | TAGS
#region-us
| I like star
| [] | [
"TAGS\n#region-us \n"
] |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | DrishtiSharma/lwg_pokemon | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T20:00:54+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
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. -->
# cwan6830/bert-finetuned-ard
This model is a fine-tuned version of [cwan6830/bert-finetuned-ner](https://huggingface.co/cwan6830/bert-f... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "cwan6830/bert-finetuned-ard", "results": []}]} | cwan6830/bert-finetuned-ard | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T20:41:40+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| cwan6830/bert-finetuned-ard
===========================
This model is a fine-tuned version of cwan6830/bert-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0493
* Validation Loss: 0.0791
* Epoch: 2
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'c... |
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. -->
# evanz37/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an u... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "evanz37/bert-finetuned-ner", "results": []}]} | evanz37/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T20:48:14+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| evanz37/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0202
* Validation Loss: 0.0603
* Epoch: 2
Model description
-----------------
More information needed
In... | [
"### 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': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
null | transformers |
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Train... | {"license": "mit", "tags": ["huggan", "gan"]} | DrishtiSharma/lwg_cartoon_faces | null | [
"transformers",
"huggan",
"gan",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T21:05:52+00:00 | [] | [] | TAGS
#transformers #huggan #gan #license-mit #endpoints_compatible #region-us
|
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you ini... | [
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the data you used to... | [
"TAGS\n#transformers #huggan #gan #license-mit #endpoints_compatible #region-us \n",
"# MyModelName",
"## Model description\n\nDescribe the model here (what it does, what it's used for, etc.)",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent is... |
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. -->
# liyingz/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "liyingz/bert-finetuned-ner", "results": []}]} | liyingz/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T21:30:14+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| liyingz/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0227
* Validation Loss: 0.0646
* Epoch: 2
Model description
-----------------
More information needed
Inte... | [
"### 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': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746
## Validation Metrics
- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555... | {"language": "unk", "tags": "autotrain", "datasets": ["js3078/autotrain-data-BerTweet"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 4.093939667345746} | js3078/autotrain-BerTweet-749522913 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
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"unk",
"dataset:js3078/autotrain-data-BerTweet",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T21:31:19+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #unk #dataset-js3078/autotrain-data-BerTweet #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 749522913
- CO2 Emissions (in grams): 4.093939667345746
## Validation Metrics
- Loss: 0.6473096609115601
- Accuracy: 0.75
- Macro F1: 0.7506205181665155
- Micro F1: 0.75
- Weighted F1: 0.7506205181665155
- Macro Precision: 0.7555... | [
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"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 749522913\n- CO2 Emissions (... |
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. -->
# evanz37/bert-finetuned-ard
This model is a fine-tuned version of [evanz37/bert-finetuned-ner](https://huggingface.co/evanz37/bert-fine... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "evanz37/bert-finetuned-ard", "results": []}]} | evanz37/bert-finetuned-ard | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T21:44:08+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| evanz37/bert-finetuned-ard
==========================
This model is a fine-tuned version of evanz37/bert-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0722
* Validation Loss: 0.0861
* Epoch: 2
Model description
-----------------
More information ne... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'class\\_name': '... |
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. -->
# JimmyWu/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "JimmyWu/bert-finetuned-ner", "results": []}]} | JimmyWu/bert-finetuned-ner | null | [
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"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T21:48:21+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| JimmyWu/bert-finetuned-ner
==========================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0086
* Validation Loss: 0.0791
* Epoch: 4
Model description
-----------------
More information needed
Inte... | [
"### 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': 1695, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
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. -->
# Krishadow/biobert-finetuned-ner-K2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Krishadow/biobert-finetuned-ner-K2", "results": []}]} | Krishadow/biobert-finetuned-ner-K2 | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T22:02:02+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Krishadow/biobert-finetuned-ner-K2
==================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0107
* Validation Loss: 0.0671
* Epoch: 4
Model description
-----------------
More informa... | [
"### 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': 1695, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
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-uncased](https://huggingface.co/bert-base-uncased) on the nc... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | kalex/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T22:04:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-uncased on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0591
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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #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: 2... |
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. -->
# lideming7757/tac-bert-finetuned-ner
This model is a fine-tuned version of [lideming7757/bert-finetuned-ner](https://huggingface.co/lid... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "lideming7757/tac-bert-finetuned-ner", "results": []}]} | lideming7757/tac-bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T22:25:48+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| lideming7757/tac-bert-finetuned-ner
===================================
This model is a fine-tuned version of lideming7757/bert-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0574
* Validation Loss: 0.0812
* Epoch: 2
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': 1e-05, 'decay\\_steps': 750, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
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. -->
# jiaxin97/bert_finetuned_ner_custom
This model is a fine-tuned version of [jiaxin97/bert-finetuned-ner](https://huggingface.co/jiaxin97... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "jiaxin97/bert_finetuned_ner_custom", "results": []}]} | jiaxin97/bert_finetuned_ner_custom | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-16T23:06:57+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| jiaxin97/bert\_finetuned\_ner\_custom
=====================================
This model is a fine-tuned version of jiaxin97/bert-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1479
* Validation Loss: 0.1963
* Epoch: 2
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': 666, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
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. -->
# ytsai25/bert-finetuned-ner-ADR
This model is a fine-tuned version of [ytsai25/bert-finetuned-ner](https://huggingface.co/ytsai25/bert-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ytsai25/bert-finetuned-ner-ADR", "results": []}]} | ytsai25/bert-finetuned-ner-ADR | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T00:01:37+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ytsai25/bert-finetuned-ner-ADR
==============================
This model is a fine-tuned version of ytsai25/bert-finetuned-ner on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0347
* Validation Loss: 0.0804
* Epoch: 2
Model description
-----------------
More inform... | [
"### 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': 669, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
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. -->
# jiaxin97/bert-finetuned-ner-ADR
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "jiaxin97/bert-finetuned-ner-ADR", "results": []}]} | jiaxin97/bert-finetuned-ner-ADR | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T00:18:16+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| jiaxin97/bert-finetuned-ner-ADR
===============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.1574
* Validation Loss: 0.1956
* Epoch: 2
Model description
-----------------
More information n... | [
"### 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': 666, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_nam... |
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. -->
# layoutlmv2-finetuned-cord
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micro... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-cord", "results": []}]} | speydach/layoutlmv2-finetuned-cord | null | [
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T01:00:43+00:00 | [] | [] | TAGS
#transformers #pytorch #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-cord
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# layoutlmv2-finetuned-cord\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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",
"## T... | [
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-cord\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.",
"## Model ... |
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. -->
# lideming7757/bert-finetuned-ner-uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-unc... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "lideming7757/bert-finetuned-ner-uncased", "results": []}]} | lideming7757/bert-finetuned-ner-uncased | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T01:24:12+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| lideming7757/bert-finetuned-ner-uncased
=======================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0240
* Validation Loss: 0.0568
* Epoch: 2
Model description
-----------------
Mo... | [
"### 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': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_nam... |
image-classification | transformers |
# anomaly
Anomaly classification
## Example Images
#### Abnormal
#### Normal
 | {"tags": ["image-classification", "pytorch"], "metrics": ["accuracy"]} | hafidber/anomaly | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T01:54:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# anomaly
Anomaly classification
## Example Images
#### Abnormal
!abnormal
#### Normal
!normal | [
"# anomaly\n\n\n\nAnomaly classification",
"## Example Images",
"#### Abnormal\n\n!abnormal",
"#### Normal\n\n!normal"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# anomaly\n\n\n\nAnomaly classification",
"## Example Images",
"#### Abnormal\n\n!abnormal",
"#### Normal\n\n!normal"
] |
fill-mask | transformers |
# roberta-base-serbian
## Model Description
This is a RoBERTa model in Serbian (Cyrillic and Latin) pre-trained on [srWaC](http://hdl.handle.net/11356/1063). You can fine-tune `roberta-base-serbian` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-serbian-upos), dependenc... | {"language": ["sr"], "license": "cc-by-sa-4.0", "tags": ["serbian", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"} | KoichiYasuoka/roberta-base-serbian | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"serbian",
"masked-lm",
"sr",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T02:15:55+00:00 | [] | [
"sr"
] | TAGS
#transformers #pytorch #roberta #fill-mask #serbian #masked-lm #sr #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-serbian
## Model Description
This is a RoBERTa model in Serbian (Cyrillic and Latin) pre-trained on srWaC. You can fine-tune 'roberta-base-serbian' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
## How to Use
| [
"# roberta-base-serbian",
"## Model Description\n\nThis is a RoBERTa model in Serbian (Cyrillic and Latin) pre-trained on srWaC. You can fine-tune 'roberta-base-serbian' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.",
"## How to Use"
] | [
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"# roberta-base-serbian",
"## Model Description\n\nThis is a RoBERTa model in Serbian (Cyrillic and Latin) pre-trained on srWaC. You can fine-tune 'roberta-... |
token-classification | transformers | SpanBert finetuned on pheno dataset for named entity recognition task | {} | sadaqabdo/SpanBert-base-pheno | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T02:17:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| SpanBert finetuned on pheno dataset for named entity recognition task | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# roberta-base-serbian-upos
## Model Description
This is a RoBERTa model in Serbian (Cyrillic and Latin) for POS-tagging and dependency-parsing, derived from [roberta-base-serbian](https://huggingface.co/KoichiYasuoka/roberta-base-serbian). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Un... | {"language": ["sr"], "license": "cc-by-sa-4.0", "tags": ["serbian", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u0414\u0430 \u0438\u043c\u0430 \u0441\u0438\u0440\u0430 \u0438 \u043c\u0430\u0441\u043b\u0430 \u... | KoichiYasuoka/roberta-base-serbian-upos | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"serbian",
"pos",
"dependency-parsing",
"sr",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T02:26:33+00:00 | [] | [
"sr"
] | TAGS
#transformers #pytorch #roberta #token-classification #serbian #pos #dependency-parsing #sr #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-serbian-upos
## Model Description
This is a RoBERTa model in Serbian (Cyrillic and Latin) for POS-tagging and dependency-parsing, derived from roberta-base-serbian. Every word is tagged by UPOS (Universal Part-Of-Speech).
## How to Use
or
## See Also
esupar: Tokenizer POS-tagger and Dependency... | [
"# roberta-base-serbian-upos",
"## Model Description\n\nThis is a RoBERTa model in Serbian (Cyrillic and Latin) for POS-tagging and dependency-parsing, derived from roberta-base-serbian. Every word is tagged by UPOS (Universal Part-Of-Speech).",
"## How to Use\n\n\n\nor",
"## See Also\n\nesupar: Tokenizer POS... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #serbian #pos #dependency-parsing #sr #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-serbian-upos",
"## Model Description\n\nThis is a RoBERTa model in Serbian (Cyrilli... |
question-answering | transformers |
# BERT BASE (cased) finetuned on Bulgarian squad data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it d... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/bert-base-squad-theseus-bg | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:2002.02925",
"license:mit",
"region:us"
] | null | 2022-04-17T02:33:52+00:00 | [
"1810.04805",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #bert #question-answering #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #region-us
|
# BERT BASE (cased) finetuned on Bulgarian squad data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it does make a difference
between bulgarian and Bulgarian. The training data is Bul... | [
"# BERT BASE (cased) finetuned on Bulgarian squad data\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it does make a difference\nbetween bulgarian and Bulgarian. The training da... | [
"TAGS\n#transformers #pytorch #bert #question-answering #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #region-us \n",
"# BERT BASE (cased) finetuned on Bulgarian squad data\n\nPretrained model on Bulgarian language using a masked language modeling ... |
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. -->
# tiny-bert-mnli-distilled
It achieves the following results on the evaluation set:
- Loss: 1.5018
- Accuracy: 0.5819
- F1 score: ... | {"tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "tiny-bert-mnli-distilled", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}, "metrics": [{"type": "accuracy", "val... | nbhimte/tiny-bert-mnli-distilled | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T02:40:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #model-index #autotrain_compatible #endpoints_compatible #region-us
| tiny-bert-mnli-distilled
========================
It achieves the following results on the evaluation set:
* Loss: 1.5018
* Accuracy: 0.5819
* F1 score: 0.5782
* Precision score: 0.6036
* Metric recall: 0.5819
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 32\n* seed: 33\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #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: 0.0005\n* train\\... |
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. -->
# Important Note:
`load_best_model_at_end` is not working properly (I specified `metric_for_best_model` on another training but it ... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "DSPFirst-Finetuning-4", "results": []}]} | ptran74/DSPFirst-Finetuning-4 | null | [
"transformers",
"pytorch",
"electra",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T02:48:01+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us
| Important Note:
===============
'load\_best\_model\_at\_end' is not working properly (I specified 'metric\_for\_best\_model' on another training but it still does not work), but the training results still show a valid trend.
DSPFirst-Finetuning-4
=====================
This model is a fine-tuned version of ahotrod... | [
"### Before fine-tuning:",
"### After fine-tuning:\n\n\nDataset\n=======\n\n\nA visualization of the dataset can be found here. \n\nThe split between train and test is 70% and 30% respectively.\n\n\nIntended uses & limitations\n---------------------------\n\n\nThis model is fine-tuned to answer questions from th... | [
"TAGS\n#transformers #pytorch #electra #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Before fine-tuning:",
"### After fine-tuning:\n\n\nDataset\n=======\n\n\nA visualization of the dataset can be found here. \n\nThe split between train and test is 70% and 30% respective... |
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. -->
# khan27/bert-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an un... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "khan27/bert-finetuned-ner", "results": []}]} | khan27/bert-finetuned-ner | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T02:53:26+00:00 | [] | [] | TAGS
#transformers #tf #bert #token-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| khan27/bert-finetuned-ner
=========================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0241
* Validation Loss: 0.0572
* Epoch: 2
Model description
-----------------
More information needed
Inte... | [
"### 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': 1017, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': ... | [
"TAGS\n#transformers #tf #bert #token-classification #generated_from_keras_callback #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: {'name': 'AdamWeightDecay', 'learning\\_... |
fill-mask | transformers |
# BERT BASE (cased)
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it does make a difference
between bulg... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/bert-base-theseus-bg | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:2002.02925",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-17T02:54:38+00:00 | [
"1810.04805",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #bert #fill-mask #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us
|
# BERT BASE (cased)
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it does make a difference
between bulgarian and Bulgarian. The training data is Bulgarian text from OSCAR, Chitanka a... | [
"# BERT BASE (cased)\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it does make a difference\nbetween bulgarian and Bulgarian. The training data is Bulgarian text from OSCAR, C... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us \n",
"# BERT BASE (cased)\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It w... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlsr-wav2vec2-base-commonvoice-demo-colab-1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xlsr-wav2vec2-base-commonvoice-demo-colab-1", "results": []}]} | chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T03:24:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xlsr-wav2vec2-base-commonvoice-demo-colab-1
===========================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3736
* Wer: 0.5517
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
fill-mask | transformers |
# BERT BASE (cased)
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is cased: it does make a difference
between bulg... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/bert-base-bg | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:1905.07213",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-17T03:27:20+00:00 | [
"1810.04805",
"1905.07213"
] | [
"bg"
] | TAGS
#transformers #pytorch #bert #fill-mask #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-1905.07213 #license-mit #autotrain_compatible #region-us
|
# BERT BASE (cased)
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it does make a difference
between bulgarian and Bulgarian.
## Model description
The model was trained similarly to ... | [
"# BERT BASE (cased)\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it does make a difference\nbetween bulgarian and Bulgarian.",
"## Model description\n\nThe model was traine... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-1905.07213 #license-mit #autotrain_compatible #region-us \n",
"# BERT BASE (cased)\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It w... |
text-classification | transformers |
# BERT BASE (cased) finetuned on Bulgarian natural-language-inference data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false} | rmihaylov/bert-base-nli-theseus-bg | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"torch",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:1810.04805",
"arxiv:2002.02925",
"license:mit",
"autotrain_compatible",
"region:us"
] | null | 2022-04-17T04:18:41+00:00 | [
"1810.04805",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #bert #text-classification #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us
|
# BERT BASE (cased) finetuned on Bulgarian natural-language-inference data
Pretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is cased: it does make a difference
between bulgarian and Bulgarian. The... | [
"# BERT BASE (cased) finetuned on Bulgarian natural-language-inference data\n\nPretrained model on Bulgarian language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it does make a difference\nbetween bulgarian and Bulga... | [
"TAGS\n#transformers #pytorch #bert #text-classification #torch #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-1810.04805 #arxiv-2002.02925 #license-mit #autotrain_compatible #region-us \n",
"# BERT BASE (cased) finetuned on Bulgarian natural-language-inference data\n\nPretrained model on Bulgaria... |
image-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. -->
# vit-base-patch16-224-in21k-shiba-inu-detector
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-in21k-shiba-inu-detector", "results": []}]} | domluna/vit-base-patch16-224-in21k-shiba-inu-detector | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T04:23:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| vit-base-patch16-224-in21k-shiba-inu-detector
=============================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on dataset with 4 dog types including Shiba Inu.
It achieves the following results on the evaluation set:
* Loss: 0.6511
* Accuracy: 1.0
Model descr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #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: 5e-05\n* train\\_batch\\... |
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. -->
# kobigbird-bert-base-finetuned-klue
This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/m... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "kobigbird-bert-base-finetuned-klue", "results": []}]} | ToToKr/kobigbird-bert-base-finetuned-klue | null | [
"transformers",
"pytorch",
"tensorboard",
"big_bird",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T06:32:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #big_bird #question-answering #generated_from_trainer #endpoints_compatible #region-us
| kobigbird-bert-base-finetuned-klue
==================================
This model is a fine-tuned version of monologg/kobigbird-bert-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8347
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 20",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #big_bird #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n*... |
text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | ScyKindness/Hatsune_Miku | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"conversational",
"arxiv:1911.00536",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T07:02:17+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] |
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. -->
# finetuning-sentiment-model-3000-samples-5pm
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples-5pm", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "p... | ttwj-sutd/finetuning-sentiment-model-3000-samples-5pm | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T07:59:22+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| finetuning-sentiment-model-3000-samples-5pm
===========================================
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: 0.4325
* Accuracy: 0.88
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: 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: 4",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #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 | Amharic Language Language Model
#Trained in Roberta architecture | {"license": "mit"} | surafelkindu/AmBERT | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T08:20:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Amharic Language Language Model
#Trained in Roberta architecture | [] | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #fill-mask #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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. -->
# mt5-zh-ja-en-trimmed-fine-tuned-v1
This model is a fine-tuned version of [K024/mt5-zh-ja-en-trimmed](https://huggingface.co/K024... | {"license": "cc-by-nc-sa-4.0", "tags": ["translation", "generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-zh-ja-en-trimmed-fine-tuned-v1", "results": []}]} | engmatic-earth/mt5-zh-ja-en-trimmed-fine-tuned-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T09:01:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #translation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# mt5-zh-ja-en-trimmed-fine-tuned-v1
This model is a fine-tuned version of K024/mt5-zh-ja-en-trimmed on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0225
- Bleu: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
"# mt5-zh-ja-en-trimmed-fine-tuned-v1\n\nThis model is a fine-tuned version of K024/mt5-zh-ja-en-trimmed on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 4.0225\n- Bleu: 0.0",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inform... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #translation #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# mt5-zh-ja-en-trimmed-fine-tuned-v1\n\nThis model is a fine-tuned version of K024/... |
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. -->
# finetuning-sentiment-model-3000-samples-6pm
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples-6pm", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb... | ttwj-sutd/finetuning-sentiment-model-3000-samples-6pm | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T09:12:28+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| finetuning-sentiment-model-3000-samples-6pm
===========================================
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: 0.2896
* Precision: 0.875
* Recall: 0.8867
* F1: 0.8808
* Accuracy: 0.88
Model... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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: 11",
"### Train... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-imdb #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: 1e-0... |
text-generation | transformers | dont worry about it
---
language:
- en
tags:
- conversational
datasets:
- basement
--- | {} | varinner/jaredbotmark1point5 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T10:07:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| dont worry about it
---
language:
- en
tags:
- conversational
datasets:
- basement
--- | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# distilroberta-base-SmithsModel2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-base-SmithsModel2", "results": []}]} | stevems1/distilroberta-base-SmithsModel2 | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T10:21:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-base-SmithsModel2
===============================
This model is a fine-tuned version of distilroberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4012
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 #roberta #fill-mask #generated_from_trainer #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\\_size: ... |
text-to-image | pytorch |
# Distill CLOOB-conditioned Latent Diffusion trained on WikiArt
## Model description
This is a smaller version of [this model](https://huggingface.co/huggan/ccld_wa), which is a cloob-conditioned latent diffusion model fine-tuned on the [WikiArt dataset](https://huggingface.co/datasets/huggan/wikiart), reducing the ... | {"license": "mit", "library_name": "pytorch", "tags": ["huggan", "diffusion", "text-to-image"], "datasets": ["huggan/wikiart"], "task": "conditional-image-generation"} | huggan/distill-ccld-wa | null | [
"pytorch",
"huggan",
"diffusion",
"text-to-image",
"dataset:huggan/wikiart",
"arxiv:2112.10752",
"license:mit",
"has_space",
"region:us"
] | null | 2022-04-17T10:34:20+00:00 | [
"2112.10752"
] | [] | TAGS
#pytorch #huggan #diffusion #text-to-image #dataset-huggan/wikiart #arxiv-2112.10752 #license-mit #has_space #region-us
|
# Distill CLOOB-conditioned Latent Diffusion trained on WikiArt
## Model description
This is a smaller version of this model, which is a cloob-conditioned latent diffusion model fine-tuned on the WikiArt dataset, reducing the latent diffusion model size from 1.2B parameters to 105M parameters with a knowledge distil... | [
"# Distill CLOOB-conditioned Latent Diffusion trained on WikiArt",
"## Model description\n\nThis is a smaller version of this model, which is a cloob-conditioned latent diffusion model fine-tuned on the WikiArt dataset, reducing the latent diffusion model size from 1.2B parameters to 105M parameters with a knowle... | [
"TAGS\n#pytorch #huggan #diffusion #text-to-image #dataset-huggan/wikiart #arxiv-2112.10752 #license-mit #has_space #region-us \n",
"# Distill CLOOB-conditioned Latent Diffusion trained on WikiArt",
"## Model description\n\nThis is a smaller version of this model, which is a cloob-conditioned latent diffusion m... |
image-classification | fastai |
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here... | {"license": "gpl-3.0", "tags": ["fastai", "image-classification"]} | fastai/fastbook_02_bears_classifier | null | [
"fastai",
"image-classification",
"license:gpl-3.0",
"region:us"
] | null | 2022-04-17T11:17:41+00:00 | [] | [] | TAGS
#fastai #image-classification #license-gpl-3.0 #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Fa... | [
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on... | [
"TAGS\n#fastai #image-classification #license-gpl-3.0 #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit u... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 751422966
- CO2 Emissions (in grams): 12.236769332727217
## Validation Metrics
- Loss: 0.1358409821987152
- Accuracy: 0.9397905759162304
- Macro F1: 0.9096049124431982
- Micro F1: 0.9397905759162304
- Weighted F1: 0.9395954853807... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-emo_carer_nojoylove"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 12.236769332727217} | crcb/emo_nojoylove | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-emo_carer_nojoylove",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T13:12:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-emo_carer_nojoylove #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 751422966
- CO2 Emissions (in grams): 12.236769332727217
## Validation Metrics
- Loss: 0.1358409821987152
- Accuracy: 0.9397905759162304
- Macro F1: 0.9096049124431982
- Micro F1: 0.9397905759162304
- Weighted F1: 0.9395954853807... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 751422966\n- CO2 Emissions (in grams): 12.236769332727217",
"## Validation Metrics\n\n- Loss: 0.1358409821987152\n- Accuracy: 0.9397905759162304\n- Macro F1: 0.9096049124431982\n- Micro F1: 0.9397905759162304\n- Weighted F... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-emo_carer_nojoylove #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 751422966\n- CO2 Emissi... |
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... | AJGP/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T13:13:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0598
* Precision: 0.9355
* Recall: 0.9512
* F1: 0.9433
* Accuracy: 0.9869
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 #tensorboard #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... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 751422974
- CO2 Emissions (in grams): 2.370895196595982
## Validation Metrics
- Loss: 0.15362708270549774
- Accuracy: 0.9345549738219895
- Macro F1: 0.9016011681330569
- Micro F1: 0.9345549738219895
- Weighted F1: 0.9345413976263... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-emo_carer_nojoylove"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 2.370895196595982} | crcb/carer_2 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-emo_carer_nojoylove",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T13:13:16+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-crcb/autotrain-data-emo_carer_nojoylove #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 751422974
- CO2 Emissions (in grams): 2.370895196595982
## Validation Metrics
- Loss: 0.15362708270549774
- Accuracy: 0.9345549738219895
- Macro F1: 0.9016011681330569
- Micro F1: 0.9345549738219895
- Weighted F1: 0.9345413976263... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 751422974\n- CO2 Emissions (in grams): 2.370895196595982",
"## Validation Metrics\n\n- Loss: 0.15362708270549774\n- Accuracy: 0.9345549738219895\n- Macro F1: 0.9016011681330569\n- Micro F1: 0.9345549738219895\n- Weighted F... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain #en #dataset-crcb/autotrain-data-emo_carer_nojoylove #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 751422974\n- CO2 Emi... |
null | txtai |
# T5-small finedtuned to generate txtai SQL
[T5 small](https://huggingface.co/t5-small) fine-tuned to generate [txtai](https://github.com/neuml/txtai) SQL. This model takes natural language queries and builds txtai-compatible SQL statements.
txtai supports both natural language queries
```
Tell me a feel good story... | {"language": "en", "license": "apache-2.0", "library_name": "txtai", "widget": [{"text": "translate English to SQL: Tell me a feel good story over last day", "example_title": "Last day 1"}, {"text": "translate English to SQL: feel good story since yesterday", "example_title": "Last day 2"}, {"text": "translate English ... | NeuML/t5-small-txtsql | null | [
"txtai",
"pytorch",
"t5",
"en",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-04-17T13:23:21+00:00 | [] | [
"en"
] | TAGS
#txtai #pytorch #t5 #en #license-apache-2.0 #has_space #region-us
|
# T5-small finedtuned to generate txtai SQL
T5 small fine-tuned to generate txtai SQL. This model takes natural language queries and builds txtai-compatible SQL statements.
txtai supports both natural language queries
and SQL statements
This model bridges the gap between the two and enables natural language qu... | [
"# T5-small finedtuned to generate txtai SQL\n\nT5 small fine-tuned to generate txtai SQL. This model takes natural language queries and builds txtai-compatible SQL statements.\n\ntxtai supports both natural language queries\n\n\n\nand SQL statements\n\n\n\nThis model bridges the gap between the two and enables nat... | [
"TAGS\n#txtai #pytorch #t5 #en #license-apache-2.0 #has_space #region-us \n",
"# T5-small finedtuned to generate txtai SQL\n\nT5 small fine-tuned to generate txtai SQL. This model takes natural language queries and builds txtai-compatible SQL statements.\n\ntxtai supports both natural language queries\n\n\n\nand ... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rutoxicity-classification
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "rutoxicity-classification", "results": []}]} | npleshkanov/rutoxicity-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T13:37:06+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #endpoints_compatible #region-us
|
# rutoxicity-classification
This model is a fine-tuned version of DeepPavlov/rubert-base-cased on the Russian Language Toxic Comments dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2747
- Acc: 0.9255
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# rutoxicity-classification\n\nThis model is a fine-tuned version of DeepPavlov/rubert-base-cased on the Russian Language Toxic Comments dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2747\n- Acc: 0.9255",
"## Model description\n\nMore information needed",
"## Intended uses & lim... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #endpoints_compatible #region-us \n",
"# rutoxicity-classification\n\nThis model is a fine-tuned version of DeepPavlov/rubert-base-cased on the Russian Language Toxic Comments dataset.\nIt achieves the following results on the evaluation set... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-zh-finetuned-0-to-1
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsink... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "opus-mt-en-zh-finetuned-0-to-1", "results": []}]} | leung233/opus-mt-en-zh-finetuned-0-to-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T14:08:03+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# opus-mt-en-zh-finetuned-0-to-1
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Train... | [
"# opus-mt-en-zh-finetuned-0-to-1\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh 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",
"## Tra... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# opus-mt-en-zh-finetuned-0-to-1\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on an unknown dataset.",
"## Mode... |
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. -->
# layoutlmv2-finetuned-cord2
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micr... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlmv2-finetuned-cord2", "results": []}]} | speydach/layoutlmv2-finetuned-cord2 | null | [
"transformers",
"pytorch",
"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T14:33:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlmv2-finetuned-cord2
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tr... | [
"# layoutlmv2-finetuned-cord2\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased 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",
"## ... | [
"TAGS\n#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# layoutlmv2-finetuned-cord2\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset."... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1057348595664519168/ZtEK... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/shaq-shaqtin/1650210626298/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/shaq-shaqtin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T14:47:12+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Shaqtin' a Fool & SHAQ.SOL
@shaq-shaqtin
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Trai... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers |
# Wav2Vec2-Conformer-Large with Relative Position Embeddings
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-conformer-rel-pos-large | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T14:54:03+00:00 | [
"2010.05171"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #pretraining #speech #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Conformer-Large with Relative Position Embeddings
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was... | [
"# Wav2Vec2-Conformer-Large with Relative Position Embeddings\n\nWav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer a... | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #pretraining #speech #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Conformer-Large with Relative Position Embeddings\n\nWav2Vec2 Conformer with relative position embeddings, pretrained on 960 h... |
text-generation | transformers |
# GPT-2 for Music
Language Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation.
This model is a rather small instance of GPT-2 trained the [Lakhclean dataset](https://colinraffel.com/projects/lmd/). The model ... | {"tags": ["gpt2", "text-generation", "music-modeling", "music-generation"], "widget": [{"text": "PIECE_START"}, {"text": "PIECE_START PIECE_START TRACK_START INST=34 DENSITY=8"}, {"text": "PIECE_START TRACK_START INST=1"}]} | ai-guru/lakhclean_mmmtrack_4bars_d-2048 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"music-modeling",
"music-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T16:24:51+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #music-modeling #music-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# GPT-2 for Music
Language Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation.
This model is a rather small instance of GPT-2 trained the Lakhclean dataset. The model generates 4 bars at a time at a 16th note... | [
"# GPT-2 for Music\n\nLanguage Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as texts, effectively reducing the task to Language Generation.\n\nThis model is a rather small instance of GPT-2 trained the Lakhclean dataset. The model generates 4 bars at a time at a 16... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #music-modeling #music-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT-2 for Music\n\nLanguage Models such as GPT-2 can be used for Music Generation. The idea is to represent pieces of music as... |
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. -->
# claim-spotter-multilingual
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "claim-spotter-multilingual", "results": []}]} | gzomer/claim-spotter-multilingual | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-17T16:33:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| claim-spotter-multilingual
==========================
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3285
* F1: 0.7996
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 2",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_s... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln37")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln37")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln37 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T16:47:54+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | aaaacash/DialoGPT-large-michaelscott | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T17:17:33+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Michael Scott DialoGPT Model | [
"# Michael Scott DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT Model"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1515513843216171009/zT6m... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/crowsunflower-holyhorror8-witheredstrings/1650220124956/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/crowsunflower-holyhorror8-witheredstrings | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-17T17:27:15+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
VacuumF & Jude obscura & The Mad Puppet/Prophet
@crowsunflower-holyhorror8-witheredstrings
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the mo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... | xysmalobia/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T17:59:35+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2161
* Accuracy: 0.923
* F1: 0.9227
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during... |
unconditional-image-generation | null | The model provided is a PGGAN generator trained on the celebahq dataset with a resolution of 1024px. It is uploaded as part of porting this project: https://github.com/genforce/sefa to hugginface spaces. | {"license": "apache-2.0", "tags": ["gan", "pggan", "huggan", "unconditional-image-generation"]} | huggan/pggan-celebahq-1024 | null | [
"pytorch",
"gan",
"pggan",
"huggan",
"unconditional-image-generation",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-04-17T18:15:25+00:00 | [] | [] | TAGS
#pytorch #gan #pggan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us
| The model provided is a PGGAN generator trained on the celebahq dataset with a resolution of 1024px. It is uploaded as part of porting this project: URL to hugginface spaces. | [] | [
"TAGS\n#pytorch #gan #pggan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us \n"
] |
unconditional-image-generation | null |
The model provided is a StyleGAN generator trained on Anime faces with a resolution of 512px. It is uploaded as part of porting this project: https://github.com/genforce/sefa to hugginface spaces. | {"license": "apache-2.0", "tags": ["gan", "stylegan", "huggan", "unconditional-image-generation"]} | huggan/stylegan_animeface512 | null | [
"pytorch",
"gan",
"stylegan",
"huggan",
"unconditional-image-generation",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-04-17T18:32:12+00:00 | [] | [] | TAGS
#pytorch #gan #stylegan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us
|
The model provided is a StyleGAN generator trained on Anime faces with a resolution of 512px. It is uploaded as part of porting this project: URL to hugginface spaces. | [] | [
"TAGS\n#pytorch #gan #stylegan #huggan #unconditional-image-generation #license-apache-2.0 #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-bemba-15hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-300m-bemba-15hrs", "results": []}]} | csikasote/xls-r-300m-bemba-15hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T19:30:49+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-bemba-15hrs
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2754
* Wer: 0.3481
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
text-classification | transformers |
# distilroberta-current
This model classifies articles as current (covering or discussing current events) or not current (not relating to current events).
The model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on a dataset of articles labeled using weak-supervision and m... | {"license": "other", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilroberta-current", "results": []}]} | valurank/distilroberta-current | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T20:29:49+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #autotrain_compatible #endpoints_compatible #region-us
| distilroberta-current
=====================
This model classifies articles as current (covering or discussing current events) or not current (not relating to current events).
The model is a fine-tuned version of distilroberta-base on a dataset of articles labeled using weak-supervision and manual labeling
It achi... | [
"### 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: 12345\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-other #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* eva... |
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. -->
# TESTING
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown data... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "accuracy", "f1"], "model-index": [{"name": "TESTING", "results": []}]} | NoCaptain/TESTING | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T21:11:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| TESTING
=======
This model is a fine-tuned version of distilroberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1167
* Precision: 0.9561
* Accuracy: 0.9592
* F1: 0.9592
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: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #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\\_batc... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "model-index": [{"name": "pegasus-samsum", "results": []}]} | danhsf/pegasus-samsum | null | [
"transformers",
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-17T21:17:42+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us
| pegasus-samsum
==============
This model is a fine-tuned version of google/pegasus-cnn\_dailymail on the samsum dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4844
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 752122994
- CO2 Emissions (in grams): 5.301132895184483
## Validation Metrics
- Loss: 0.7107211351394653
- Accuracy: 0.7529411764705882
- Precision: 0.7502287282708143
- Recall: 0.9177392277560157
- AUC: 0.8358316393336287
- F1: 0.825... | {"language": "en", "tags": "autotrain", "datasets": ["crcb/autotrain-data-hate_speech"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 5.301132895184483} | crcb/hateval_re | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"en",
"dataset:crcb/autotrain-data-hate_speech",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T00:32:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-hate_speech #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 752122994
- CO2 Emissions (in grams): 5.301132895184483
## Validation Metrics
- Loss: 0.7107211351394653
- Accuracy: 0.7529411764705882
- Precision: 0.7502287282708143
- Recall: 0.9177392277560157
- AUC: 0.8358316393336287
- F1: 0.825... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 752122994\n- CO2 Emissions (in grams): 5.301132895184483",
"## Validation Metrics\n\n- Loss: 0.7107211351394653\n- Accuracy: 0.7529411764705882\n- Precision: 0.7502287282708143\n- Recall: 0.9177392277560157\n- AUC: 0.8358316393... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #en #dataset-crcb/autotrain-data-hate_speech #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Binary Classification\n- Model ID: 752122994\n- CO2 Emissions (in grams... |
fill-mask | transformers |
# Overview
This model is based on [bert-base-uncased](https://huggingface.co/bert-base-uncased) model and trained on more than 30k tweets that scraped from Twitter. By inputing some sentences with a '[MASK]' indicating the location you would like to fill in with a hashtag, our model can generate potential related tre... | {"license": "afl-3.0"} | vivianhuang88/bert_twitter_hashtag | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T00:35:57+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Overview
This model is based on bert-base-uncased model and trained on more than 30k tweets that scraped from Twitter. By inputing some sentences with a '[MASK]' indicating the location you would like to fill in with a hashtag, our model can generate potential related trending topics according to your tweet context... | [
"# Overview\n\nThis model is based on bert-base-uncased model and trained on more than 30k tweets that scraped from Twitter. By inputing some sentences with a '[MASK]' indicating the location you would like to fill in with a hashtag, our model can generate potential related trending topics according to your tweet c... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Overview\n\nThis model is based on bert-base-uncased model and trained on more than 30k tweets that scraped from Twitter. By inputing some sentences with a '[MASK]' indicating ... |
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. -->
# communication-classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "communication-classifier", "results": []}]} | joniponi/communication-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T00:46:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# communication-classifier
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1249
- eval_accuracy: 0.9644
- eval_f1: 0.9644
- eval_runtime: 2.6719
- eval_samples_per_second: 126.126
- eval_steps_per_second: 8.23... | [
"# communication-classifier\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.1249\n- eval_accuracy: 0.9644\n- eval_f1: 0.9644\n- eval_runtime: 2.6719\n- eval_samples_per_second: 126.126\n- eval_steps_per_s... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# communication-classifier\n\nThis model is a fine-tuned version of distilbert-base-uncased on the None dataset.\nIt achieves the fol... |
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. -->
# kobigbird-bert-base-finetuned-klue-goorm-q-a-task
This model is a fine-tuned version of [ToToKr/kobigbird-bert-base-finetuned-kl... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "kobigbird-bert-base-finetuned-klue-goorm-q-a-task", "results": []}]} | ToToKr/kobigbird-bert-base-finetuned-klue-goorm-q-a-task | null | [
"transformers",
"pytorch",
"big_bird",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T00:53:31+00:00 | [] | [] | TAGS
#transformers #pytorch #big_bird #question-answering #generated_from_trainer #endpoints_compatible #region-us
| kobigbird-bert-base-finetuned-klue-goorm-q-a-task
=================================================
This model is a fine-tuned version of ToToKr/kobigbird-bert-base-finetuned-klue on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2115
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20",
"### Trainin... | [
"TAGS\n#transformers #pytorch #big_bird #question-answering #generated_from_trainer #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* op... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln38")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln38")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | {} | BigSalmon/InformalToFormalLincoln38 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T01:53:09+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Keywords to sentences or sentence. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-bemba-10hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-300m-bemba-10hrs", "results": []}]} | csikasote/xls-r-300m-bemba-10hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T02:07:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-bemba-10hrs
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3022
* Wer: 0.3976
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | user1/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T02:29:32+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2302
* Accuracy: 0.9215
* F1: 0.9216
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #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* learn... |
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. -->
# AraBART-finetuned-ar-wikilingua
This model is a fine-tuned version of [moussaKam/AraBART](https://huggingface.co/moussaKam/AraBA... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "datasets": ["wiki_lingua"], "model-index": [{"name": "AraBART-finetuned-ar-wikilingua", "results": []}]} | eslamxm/AraBART-finetuned-ar-wikilingua | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:wiki_lingua",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T02:49:25+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| AraBART-finetuned-ar-wikilingua
===============================
This model is a fine-tuned version of moussaKam/AraBART on the wiki\_lingua dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9990
* Rouge-1: 23.82
* Rouge-2: 8.97
* Rouge-l: 21.05
* Gen Len: 19.06
* Bertscore: 72.08
Model d... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #summarization #generated_from_trainer #dataset-wiki_lingua #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:... |
text-generation | transformers |
#Michael Scott Chatbot | {"tags": ["conversational"]} | BFMeriem/model | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T03:28:53+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Michael Scott Chatbot | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
## Model description
The rotten-tomatoes-model is a text-classification model. It used the `bert-base-cased` model, and was fine tuned on the `rotten_tomatoes` model.
After inputting a movie review, the model will output its prediction of how positive/negative the review is. `LABEL_0` is Negative, while `LABEL_1` ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmpjy56pamo", "results": []}]} | klin1/rotten-tomatoes-model | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T03:31:48+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Model description
-----------------
The rotten-tomatoes-model is a text-classification model. It used the 'bert-base-cased' model, and was fine tuned on the 'rotten\_tomatoes' model.
After inputting a movie review, the model will output its prediction of how positive/negative the review is. 'LABEL\_0' is Negative, ... | [
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* TensorFlow 2.8.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* TensorFlow 2.8.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509337156787003394/WjOd... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tojibawhiteroom/1650256419756/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tojibawhiteroom | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T03:32:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Tojiba White Room (T\_\_T).1
@tojibawhiteroom
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
T... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | azert99/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T03:38:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
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: 0.3223
- Accuracy: 0.8767
- F1: 0.8818
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3223\n- Accuracy: 0.8767\n- F1: 0.8818",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
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. -->
# facility-classifier
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "facility-classifier", "results": []}]} | joniponi/facility-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T03:50:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| facility-classifier
===================
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: 0.4422
* Accuracy: 0.7872
* F1: 0.7854
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### 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: 6",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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\\_b... |
text-generation | transformers |
This is https://huggingface.co/sberbank-ai/rugpt3large_based_on_gpt2 model, fine-tuned on the questions of CHGK (Что? Где? Когда? https://db.chgk.info/)
Dataset: 75 000 questions from 2000-2019
Trained for 5 epochs | {"language": ["ru"], "tags": ["PyTorch", "Transformers", "text-generation"], "widget": [{"text": "\u0418\u0437\u0432\u0435\u0441\u0442\u043d\u044b\u0439 \u0447\u0435\u043b\u043e\u0432\u0435\u043a"}], "inference": {"parameters": {"max_length": 60, "do_sample": true, "temperature": 0.6, "no_repeat_ngram_size": 2}}} | mary905el/rugpt3large_neuro_chgk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T03:57:14+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is URL model, fine-tuned on the questions of CHGK (Что? Где? Когда? URL
Dataset: 75 000 questions from 2000-2019
Trained for 5 epochs | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Michael Scott Character Chatbot | {"tags": ["conversational"]} | BFMeriem/chatbot-model | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T04:09:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
#Michael Scott Character Chatbot | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
sentence-similarity | transformers |
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should ... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false, "pipeline_tag": "sentence-similarity"} | rmihaylov/roberta-base-nli-stsb-theseus-bg | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"torch",
"sentence-similarity",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:2004.09813",
"arxiv:2002.02925",
"license:mit",
"region:us"
] | null | 2022-04-18T04:51:49+00:00 | [
"2004.09813",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #arxiv-2002.02925 #license-mit #region-us
|
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same location in th... | [
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences. \n\nUsing the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same locatio... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #arxiv-2002.02925 #license-mit #region-us \n",
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Rob... |
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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | dfsj/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T05:50:36+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2170
* Accuracy: 0.922
* F1: 0.9222
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #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: 2... |
null | transformers | # LayoutLMv3
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objective... | {"language": "en", "license": "cc-by-nc-sa-4.0"} | microsoft/layoutlmv3-base | null | [
"transformers",
"pytorch",
"tf",
"onnx",
"layoutlmv3",
"en",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T05:53:05+00:00 | [
"2204.08387"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #onnx #layoutlmv3 #en #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
| # LayoutLMv3
Microsoft Document AI | GitHub
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tun... | [
"# LayoutLMv3\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 ca... | [
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"# LayoutLMv3\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and ... |
null | transformers | # LayoutLMv3
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlmv3)
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objective... | {"language": "en", "license": "cc-by-nc-sa-4.0"} | microsoft/layoutlmv3-large | null | [
"transformers",
"pytorch",
"tf",
"layoutlmv3",
"en",
"arxiv:2204.08387",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T05:56:58+00:00 | [
"2204.08387"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #layoutlmv3 #en #arxiv-2204.08387 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
| # LayoutLMv3
Microsoft Document AI | GitHub
## Model description
LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tun... | [
"# LayoutLMv3\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 ca... | [
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"# LayoutLMv3\n\nMicrosoft Document AI | GitHub",
"## Model description\n\nLayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image ... |
sentence-similarity | transformers |
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should ... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false, "pipeline_tag": "sentence-similarity"} | rmihaylov/roberta-base-nli-stsb-bg | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"torch",
"sentence-similarity",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:2004.09813",
"license:mit",
"region:us"
] | null | 2022-04-18T06:02:39+00:00 | [
"2004.09813"
] | [
"bg"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #license-mit #region-us
|
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same location in th... | [
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences. \n\nUsing the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same locatio... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #license-mit #region-us \n",
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Roberta model. It cou... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1545140847259406337/bTk2... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/buckeshot-onlinepete/1662024914888/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/buckeshot-onlinepete | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-04-18T06:03:11+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
BUCKSHOT & im pete online
@buckeshot-onlinepete
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-bemba-5hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2v... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-300m-bemba-5hrs", "results": []}]} | csikasote/xls-r-300m-bemba-5hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T06:37:39+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-bemba-5hrs
=====================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3129
* Wer: 0.4430
Model description
-----------------
More information needed
Intended uses & limitations
--... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
sentence-similarity | transformers |
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should ... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false, "pipeline_tag": "sentence-similarity"} | rmihaylov/roberta-base-use-qa-bg | null | [
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"pytorch",
"xlm-roberta",
"feature-extraction",
"torch",
"sentence-similarity",
"custom_code",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:2004.09813",
"license:mit",
"region:us"
] | null | 2022-04-18T07:42:48+00:00 | [
"2004.09813"
] | [
"bg"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #license-mit #region-us
|
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same location in th... | [
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences. \n\nUsing the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same locatio... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #license-mit #region-us \n",
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Roberta ... |
null | null | # Talking Bot
A AI used for the Discord Talking Bot. That's all. | {"license": "cc"} | Furcorn/talking-bot | null | [
"license:cc",
"region:us"
] | null | 2022-04-18T08:10:04+00:00 | [] | [] | TAGS
#license-cc #region-us
| # Talking Bot
A AI used for the Discord Talking Bot. That's all. | [
"# Talking Bot\nA AI used for the Discord Talking Bot. That's all."
] | [
"TAGS\n#license-cc #region-us \n",
"# Talking Bot\nA AI used for the Discord Talking Bot. That's all."
] |
sentence-similarity | transformers |
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from [Sentence-BERT](https://arxiv.org/abs/2004.09813), the training is based on the idea that a translated sentence should ... | {"language": ["bg"], "license": "mit", "tags": ["torch"], "datasets": ["oscar", "chitanka", "wikipedia"], "inference": false, "pipeline_tag": "sentence-similarity"} | rmihaylov/roberta-base-use-qa-theseus-bg | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"torch",
"sentence-similarity",
"custom_code",
"bg",
"dataset:oscar",
"dataset:chitanka",
"dataset:wikipedia",
"arxiv:2004.09813",
"arxiv:2002.02925",
"license:mit",
"region:us"
] | null | 2022-04-18T08:12:32+00:00 | [
"2004.09813",
"2002.02925"
] | [
"bg"
] | TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #arxiv-2002.02925 #license-mit #region-us
|
# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data
This is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences.
Using the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same location in th... | [
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Multilingual Roberta model. It could be used for creating embeddings of Bulgarian sentences. \n\nUsing the ideas from Sentence-BERT, the training is based on the idea that a translated sentence should be mapped to the same locatio... | [
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #torch #sentence-similarity #custom_code #bg #dataset-oscar #dataset-chitanka #dataset-wikipedia #arxiv-2004.09813 #arxiv-2002.02925 #license-mit #region-us \n",
"# ROBERTA BASE (cased) trained on private Bulgarian-English parallel data\nThis is a Mul... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Conformer-Large-960h with Relative Position Embeddings
Wav2Vec2-Conformer with relative position embeddings, pretrained and **fine-tuned on 960 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Paper**: [fairseq S2T: Fas... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-conformer-rel-pos-large-960h-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Rec... | facebook/wav2vec2-conformer-rel-pos-large-960h-ft | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
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"arxiv:2010.05171",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T08:17:37+00:00 | [
"2010.05171"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Conformer-Large-960h with Relative Position Embeddings
===============================================================
Wav2Vec2-Conformer with relative position embeddings, pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech inp... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# Taiyi-Roberta-124M-D
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
COCO和VG上特殊预训练的，英文版的MAP（名称暂定）的文本端RoBERTa-base。
Special pre-training on COCO and VG, the textual encoder for MAP (temporary) in English, R... | {"language": ["en"], "license": "apache-2.0", "tags": ["roberta", "mutlimodal", "exbert"], "inference": false} | IDEA-CCNL/Taiyi-Roberta-124M-D | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"mutlimodal",
"exbert",
"en",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-04-18T08:20:35+00:00 | [
"2209.02970"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #mutlimodal #exbert #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #region-us
| Taiyi-Roberta-124M-D
====================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
COCO和VG上特殊预训练的，英文版的MAP（名称暂定）的文本端RoBERTa-base。
Special pre-training on COCO and VG, the textual encoder for MAP (temporary) in English, RoBERTa-base.
模型分类 Model Taxonomy
----... | [
"### 下游效果 Performance\n\n\nGLUE\n\n\n\nThe local test settings are:\nSequence length: 128, Batch size: 32, Learning rate: 3e-5\n\n\nAn additional dataset WNLI is tested.\n\n\n使用 Usage\n--------\n\n\n引用 Citation\n-----------\n\n\n如果您在您的工作中使用了我们的模型，可以引用我们的论文：\n\n\nIf you are using the resource for your work, please c... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #mutlimodal #exbert #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #region-us \n",
"### 下游效果 Performance\n\n\nGLUE\n\n\n\nThe local test settings are:\nSequence length: 128, Batch size: 32, Learning rate: 3e-5\n\n\nAn additional dataset WNLI is te... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings
[Facebook's Wav2Vec2 Conformer (TODO-add link)]()
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-conformer-rel-pos-large-100h-ft | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T08:26:04+00:00 | [
"2010.05171"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings
[Facebook's Wav2Vec2 Conformer (TODO-add link)]()
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sur... | [
"# Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings\n\n[Facebook's Wav2Vec2 Conformer (TODO-add link)]()\n\nWav2Vec2 Conformer with relative position embeddings, pretrained on 960h hours of Librispeech and and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model ... | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Conformer-Large-100h with Relative Position Embeddings\n\n[Facebook's Wav2Vec2 Con... |
null | transformers |
# Wav2Vec2-Conformer-Large with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-conformer-rope-large | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"pretraining",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T08:26:53+00:00 | [
"2010.05171"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #pretraining #speech #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Conformer-Large with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Note: This model does not have a tokenizer as it was pre... | [
"# Wav2Vec2-Conformer-Large with Rotary Position Embeddings\n\nWav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\nNote: This model does not have a tokenizer as it... | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #pretraining #speech #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Conformer-Large with Rotary Position Embeddings\n\nWav2Vec2 Conformer with rotary position embeddings, pretrained on 960 hours... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained and **fine-tuned on 960 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Paper**: [fairseq S2T: Fast Sp... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-conformer-rel-pos-large-960h-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Rec... | facebook/wav2vec2-conformer-rope-large-960h-ft | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-04-18T08:48:39+00:00 | [
"2010.05171"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings
=============================================================
Wav2Vec2 Conformer with rotary position embeddings, pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**P... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"]} | facebook/wav2vec2-conformer-rope-large-100h-ft | null | [
"transformers",
"pytorch",
"wav2vec2-conformer",
"automatic-speech-recognition",
"speech",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T08:48:47+00:00 | [
"2010.05171"
] | [
"en"
] | TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings
Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
Paper: ... | [
"# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings\n\nWav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on 100 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.\n\... | [
"TAGS\n#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2010.05171 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings\n\nWav2Vec2 Conformer with ro... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-300m-bemba-20hrs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "xls-r-300m-bemba-20hrs", "results": []}]} | csikasote/xls-r-300m-bemba-20hrs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T09:01:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| xls-r-300m-bemba-20hrs
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2815
* Wer: 0.3435
Model description
-----------------
More information needed
Intended uses & limitations
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-nepali
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-nepali", "results": []}]} | shishirpaudel/wav2vec2-large-xlsr-nepali | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T09:10:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-nepali
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Traini... | [
"# wav2vec2-large-xlsr-nepali\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-nepali\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.",
"## Model description... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | zoha/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T09:33:13+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... |
fill-mask | transformers |
# Twitter March 2022 (RoBERTa-base, 128M)
This is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. Fo... | {"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]} | cardiffnlp/twitter-roberta-base-mar2022 | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-04-18T09:41:05+00:00 | [
"2202.03829"
] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter March 2022 (RoBERTa-base, 128M)
This is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to c... | [
"# Twitter March 2022 (RoBERTa-base, 128M)\n\nThis is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more su... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter March 2022 (RoBERTa-base, 128M)\n\nThis is a RoBERTa-base model trained on 128.06M tweets until the end of March 2022.\... |
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