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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-eurlex
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://hu... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-eurlex", "results": []}]} | muhtasham/bert-small-finetuned-eurlex | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T20:46:44+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert_uncased_L-4_H-512_A-8-finetuned-eurlex
This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.9798
- eval_runtime: 51.2571
- eval_samples_per_second: 638.916
- eval_steps_per_second: 79.872
- epoch: ... | [
"# bert_uncased_L-4_H-512_A-8-finetuned-eurlex\n\nThis model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9798\n- eval_runtime: 51.2571\n- eval_samples_per_second: 638.916\n- eval_steps_per_second: 79.872\... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert_uncased_L-4_H-512_A-8-finetuned-eurlex\n\nThis model is a fine-tuned version of goo... |
null | null | El comisario Benavides le advirtió a Harold que de tanto andar entre poetas iba a terminar hablando como uno. Lamentablemente, la advertencia llegaba tarde, porque para ese entonces, Harold no solo hablaba como poeta sino que escribía en negro para El Tuerto Dávalos y también había tenido un affaire con una fotógrafa a... | {} | mponsigue/avgt | null | [
"region:us"
] | null | 2022-08-14T20:53:08+00:00 | [] | [] | TAGS
#region-us
| El comisario Benavides le advirtió a Harold que de tanto andar entre poetas iba a terminar hablando como uno. Lamentablemente, la advertencia llegaba tarde, porque para ese entonces, Harold no solo hablaba como poeta sino que escribía en negro para El Tuerto Dávalos y también había tenido un affaire con una fotógrafa a... | [] | [
"TAGS\n#region-us \n"
] |
null | null |
# Testing Hugging Face
Some text 3
| {"license": "apache-2.0"} | Davidg707/test_model | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-14T21:05:04+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
# Testing Hugging Face
Some text 3
| [
"# Testing Hugging Face\n\nSome text 3"
] | [
"TAGS\n#license-apache-2.0 #region-us \n",
"# Testing Hugging Face\n\nSome text 3"
] |
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-cvbn-37k
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"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2-base-cvbn-37k", "results": []}]} | MBMMurad/wav2vec2-base-cvbn-37k | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:cvbn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T21:13:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-cvbn-37k
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2288
- eval_wer: 0.3332
- eval_runtime: 329.8903
- eval_samples_per_second: 9.094
- eval_steps_per_second: 0.57
- epoch: 3.59
- ste... | [
"# wav2vec2-base-cvbn-37k\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2288\n- eval_wer: 0.3332\n- eval_runtime: 329.8903\n- eval_samples_per_second: 9.094\n- eval_steps_per_second: 0.57\n- epoch:... | [
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"# wav2vec2-base-cvbn-37k\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the f... |
null | null |
Change 2
| {"license": "apache-2.0"} | Davidg707/test2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2022-08-14T21:41:37+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
|
Change 2
| [] | [
"TAGS\n#license-apache-2.0 #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. -->
# bert_uncased_L-4_H-512_A-8-finetuned-eurlex-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert_uncased_L-4_H-512_A-8-finetuned-eurlex-longer", "results": []}]} | muhtasham/bert-small-finetuned-eurlex-longer | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T22:24:07+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert\_uncased\_L-4\_H-512\_A-8-finetuned-eurlex-longer
======================================================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8229
Model description
-----------------... | [
"### 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: 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: 10",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #base_model-google/bert_uncased_L-4_H-512_A-8 #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* learn... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-small-finetuned-cuad-full
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cuad"], "model-index": [{"name": "bert-small-finetuned-cuad-full", "results": []}]} | muhtasham/bert-small-finetuned-cuad-full | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:cuad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-14T23:00:59+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #license-apache-2.0 #endpoints_compatible #region-us
| bert-small-finetuned-cuad-full
==============================
This model is a fine-tuned version of google/bert\_uncased\_L-4\_H-512\_A-8 on the cuad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0274
Model description
-----------------
More information needed
Intended uses & lim... | [
"### 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* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-cuad #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: 2e-05\n* train\\_batch\\_size: 32... |
fill-mask | transformers | # BERT trained with YFCC15M with the same capacity with CLIP text encoder
- Training epochs 32
- Valid PPL final: 15.53
| {} | tobiaslee/bert-yfcc15m | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T01:26:49+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # BERT trained with YFCC15M with the same capacity with CLIP text encoder
- Training epochs 32
- Valid PPL final: 15.53
| [
"# BERT trained with YFCC15M with the same capacity with CLIP text encoder\n\n\n- Training epochs 32\n- Valid PPL final: 15.53"
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT trained with YFCC15M with the same capacity with CLIP text encoder\n\n\n- Training epochs 32\n- Valid PPL final: 15.53"
] |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | lmxhappy/yule_bagua_bert | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T01:46:39+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or se... |
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-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "c... | NLPtime/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T02:24:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2886
* Bleu: 28.1505
* Gen Len: 34.1036
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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #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\\... |
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-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "c... | andreypurwanto/opus-mt-en-ro-finetuned-en-to-ro | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T02:47:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| opus-mt-en-ro-finetuned-en-to-ro
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2886
* Bleu: 28.1505
* Gen Len: 34.1036
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: 1\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\... |
null | null |
Hello | {"tags": ["tag1", "tag2"]} | rosikand/test_model | null | [
"tag1",
"tag2",
"region:us"
] | null | 2022-08-15T03:38:43+00:00 | [] | [] | TAGS
#tag1 #tag2 #region-us
|
Hello | [] | [
"TAGS\n#tag1 #tag2 #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. -->
# distilhubert-ko-zeroth
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhuber... | {"language": ["ko"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "zeroth", "generated_from_trainer"], "model-index": [{"name": "distilhubert-ko-zeroth", "results": []}]} | Bingsu/distilhubert-ko-zeroth | null | [
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"zeroth",
"generated_from_trainer",
"ko",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T04:08:15+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #hubert #automatic-speech-recognition #zeroth #generated_from_trainer #ko #license-apache-2.0 #endpoints_compatible #region-us
| distilhubert-ko-zeroth
======================
This model is a fine-tuned version of ntu-spml/distilhubert on the BINGSU/ZEROTH-KOREAN - NA dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9934
* Cer: 0.2066
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #hubert #automatic-speech-recognition #zeroth #generated_from_trainer #ko #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: 0.001\n* train\\_batch\\_size: 16\n*... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **MountainCar-v0**
This is a trained model of a **DQN** agent playing **MountainCar-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "Mounta... | bdokmeci/dqn-MountainCar-v0 | null | [
"stable-baselines3",
"MountainCar-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-15T04:10:13+00:00 | [] | [] | TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing MountainCar-v0
This is a trained model of a DQN agent playing MountainCar-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing MountainCar-v0\nThis is a trained model of a DQN agent playing MountainCar-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
null | null | **PEGASUS-ClaimsKG**
PEGASUS-LARGE fine-tuned on the full [ClaimsKG](https://data.gesis.org/claimskg/) dataset.
- BERTScore:
F1 score: 0.871 || Precision score: 0.881 || Recall score: 0.864
-Rouge-1 Score(precision=0.781, recall=0.737, fmeasure=0.743)
-Rouge-2 Score(precision=0.660, recall=0.626, fmeasure=0.631)
... | {"license": "mit"} | Cosmos/PEGASUS-ClaimsKG | null | [
"license:mit",
"region:us"
] | null | 2022-08-15T04:19:03+00:00 | [] | [] | TAGS
#license-mit #region-us
| PEGASUS-ClaimsKG
PEGASUS-LARGE fine-tuned on the full ClaimsKG dataset.
- BERTScore:
F1 score: 0.871 || Precision score: 0.881 || Recall score: 0.864
-Rouge-1 Score(precision=0.781, recall=0.737, fmeasure=0.743)
-Rouge-2 Score(precision=0.660, recall=0.626, fmeasure=0.631)
-Rouge-L Score(precision=0.750, recall=0... | [] | [
"TAGS\n#license-mit #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-emotions
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotions", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotio... | YujiK/distilbert-base-uncased-finetuned-emotions | 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 | 2022-08-15T04:45:39+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-emotions
==========================================
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: 0.2133
* Accuracy: 0.9265
* F1: 0.9263
Model description
-----------------
... | [
"### 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... |
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
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["twitter-sentiment-analysis"], "metrics": ["accuracy", "precision", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tw... | riddhi17pawar/distilbert-base-uncased-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:twitter-sentiment-analysis",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T05:32:43+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-base-uncased-finetuned
This model is a fine-tuned version of distilbert-base-uncased on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4337
- Accuracy: 0.812
- Precision: 0.7910
- F1: 0.8042
## Model description
More information needed
## I... | [
"# distilbert-base-uncased-finetuned\n\nThis model is a fine-tuned version of distilbert-base-uncased on the twitter-sentiment-analysis dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4337\n- Accuracy: 0.812\n- Precision: 0.7910\n- F1: 0.8042",
"## Model description\n\nMore informati... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-base-uncased-finetuned\n\nThis model is a fine-tuned version of distilb... |
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", "config"... | Zul/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 | 2022-08-15T06:23:54+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: 0.3144
- Accuracy: 0.87
- F1: 0.8713
## Model description
More information needed
## Intended uses & limitations
More info... | [
"# 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: 0.3144\n- Accuracy: 0.87\n- F1: 0.8713",
"## Model description\n\nMore information needed",
"## Intended uses & limi... | [
"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... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/hu... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "huggan/smithsonian_butterflies_subset", "metrics": []} | knok/ddpm-butterflies-128 | null | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-08-15T06:32:50+00:00 | [] | [
"en"
] | TAGS
#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us
|
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'huggan/smithsonian_butterflies_subset' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Tr... | [
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential... | [
"TAGS\n#diffusers #tensorboard #en #dataset-huggan/smithsonian_butterflies_subset #license-apache-2.0 #diffusers-DDPMPipeline #region-us \n",
"# ddpm-butterflies-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'huggan/smithsonian_butterflies_subset' dataset.",... |
fill-mask | transformers |
# Mengzi-BERT L6-H768 model (Chinese)
This model is a distilled version of mengzi-bert-large.
## Usage
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("Langboat/mengzi-bert-L6-H768")
model = BertModel.from_pretrained("Langboat/mengzi-bert-L6-H768")
```
## Scor... | {"language": ["zh"], "license": "apache-2.0"} | Langboat/mengzi-bert-L6-H768 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"zh",
"arxiv:2110.06696",
"doi:10.57967/hf/0027",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T06:37:46+00:00 | [
"2110.06696"
] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #zh #arxiv-2110.06696 #doi-10.57967/hf/0027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Mengzi-BERT L6-H768 model (Chinese)
===================================
This model is a distilled version of mengzi-bert-large.
Usage
-----
Scores on nine chinese tasks (without any data augmentation)
------------------------------------------------------------
RoBERTa-wwm-ext scores are from CLUE baseline
I... | [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #zh #arxiv-2110.06696 #doi-10.57967/hf/0027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingf... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "... | MayaGalvez/bert-base-multilingual-cased-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:wikiann",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T07:05:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-multilingual-cased-finetuned-ner
==========================================
This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2299
* Precision: 0.8327
* Recall: 0.8515
* F1: 0.8420
* Accuracy: 0.934... | [
"### 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* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-wikiann #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-05\... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | ctoner2653/DialoGPT-medium-RickBoty | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T07:16:52+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
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. -->
# bert-base-uncased-finetuned-sentiment
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["twitter-sentiment-analysis"], "metrics": ["accuracy", "precision", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-sentiment", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name":... | riddhi17pawar/bert-base-uncased-finetuned-sentiment | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:twitter-sentiment-analysis",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T07:39:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-uncased-finetuned-sentiment
This model is a fine-tuned version of bert-base-uncased on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4630
- Accuracy: 0.814
- Precision: 0.7871
- F1: 0.8082
## Model description
More information needed
## Int... | [
"# bert-base-uncased-finetuned-sentiment\n\nThis model is a fine-tuned version of bert-base-uncased on the twitter-sentiment-analysis dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4630\n- Accuracy: 0.814\n- Precision: 0.7871\n- F1: 0.8082",
"## Model description\n\nMore information... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-twitter-sentiment-analysis #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-uncased-finetuned-sentiment\n\nThis model is a fine-tuned version of bert-base... |
fill-mask | transformers |
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_2022_logo_with_name.png" alt="RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use" width="75%">
</p>
# RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use.
RobBER... | {"language": "nl", "license": "mit", "tags": ["Dutch", "Flemish", "RoBERTa", "RobBERT"], "datasets": ["oscar", "dbrd", "lassy-ud", "europarl-mono", "conll2002"], "thumbnail": "https://github.com/iPieter/RobBERT/raw/master/res/robbert_2022_logo.png", "widget": [{"text": "Hallo, ik ben RobBERT-2022, het nieuwe <mask> taa... | DTAI-KULeuven/robbert-2022-dutch-base | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2211.08192",
"arxiv:2001.06286",
"arxiv:1907.11692",
"arxiv:... | null | 2022-08-15T08:48:36+00:00 | [
"2211.08192",
"2001.06286",
"1907.11692",
"2001.02943"
] | [
"nl"
] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2211.08192 #arxiv-2001.06286 #arxiv-1907.11692 #arxiv-2001.02943 #license-mit #autotrain_compatible #endpoints_compatible #re... |
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use.
===================================================================================
RobBERT-2022 is the latest release of the Dutch RobBERT model.
It further pretrained the original pdelobelle/robbert-v2-dutch-base m... | [
"### Our Performance Evaluation Results\n\n\nAll experiments are described in more detail in our paper, with the code in our GitHub repository.",
"### Sentiment analysis\n\n\nPredicting whether a review is positive or negative using the Dutch Book Reviews Dataset.",
"### Die/Dat (coreference resolution)\n\n\nWe... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #Dutch #Flemish #RoBERTa #RobBERT #nl #dataset-oscar #dataset-dbrd #dataset-lassy-ud #dataset-europarl-mono #dataset-conll2002 #arxiv-2211.08192 #arxiv-2001.06286 #arxiv-1907.11692 #arxiv-2001.02943 #license-mit #autotrain_compatible #endpoints_compatib... |
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-cvbn-37knew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2-base-cvbn-37knew", "results": []}]} | MBMMurad/wav2vec2-base-cvbn-37knew | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:cvbn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T08:59:01+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-cvbn-37knew
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2208
- eval_wer: 0.2889
- eval_runtime: 336.8019
- eval_samples_per_second: 8.907
- eval_steps_per_second: 0.558
- epoch: 4.11
-... | [
"# wav2vec2-base-cvbn-37knew\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2208\n- eval_wer: 0.2889\n- eval_runtime: 336.8019\n- eval_samples_per_second: 8.907\n- eval_steps_per_second: 0.558\n- ep... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-cvbn-37knew\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves th... |
text-classification | transformers | --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 5.0 --mlm \ | {} | alishudi/distil_mse_bad | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T09:39:17+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 5.0 --mlm \ | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #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. -->
# wav2vec2-base-cvbn-voted_30pochs
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cvbn"], "model-index": [{"name": "wav2vec2-base-cvbn-voted_30pochs", "results": []}]} | MBMMurad/wav2vec2-base-cvbn-voted_30pochs | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:cvbn",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T10:40:26+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-cvbn-voted_30pochs
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2136
- eval_wer: 0.3208
- eval_runtime: 335.1421
- eval_samples_per_second: 8.951
- eval_steps_per_second: 0.561
- epoch:... | [
"# wav2vec2-base-cvbn-voted_30pochs\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2136\n- eval_wer: 0.3208\n- eval_runtime: 335.1421\n- eval_samples_per_second: 8.951\n- eval_steps_per_second: 0.56... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-cvbn #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-cvbn-voted_30pochs\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the cvbn dataset.\nIt achi... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | georgio/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T10:41:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1533
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 2e-05\n* train\\_batch\\_s... |
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. -->
# wav2vec-large-xls-r-300-ha-colab_4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/f... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_10_0"], "model-index": [{"name": "wav2vec-large-xls-r-300-ha-colab_4", "results": []}]} | moro23/wav2vec-large-xls-r-300-ha-colab_4 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_10_0",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-15T10:48:01+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| wav2vec-large-xls-r-300-ha-colab\_4
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_10\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8545
* Wer: 0.5860
Model description
-----------------
More informati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_10_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n... |
null | transformers |
## Donut demo
This model is the result of fine-tuning `VisionEncoderDecoderModel` on the [naver-clova-ix/cord-v2](https://huggingface.co/datasets/naver-clova-ix/cord-v2) dataset.
The Weights and Biases report can be found [here](https://wandb.ai/nielsrogge/Donut/reports/Fine-tuning-Donut-on-CORD--VmlldzoyNDgxMzAx?ac... | {"license": "mit", "datasets": "naver-clova-ix/cord-v2"} | nielsr/donut-demo | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"dataset:naver-clova-ix/cord-v2",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T10:51:18+00:00 | [] | [] | TAGS
#transformers #pytorch #vision-encoder-decoder #dataset-naver-clova-ix/cord-v2 #license-mit #endpoints_compatible #region-us
|
## Donut demo
This model is the result of fine-tuning 'VisionEncoderDecoderModel' on the naver-clova-ix/cord-v2 dataset.
The Weights and Biases report can be found here. | [
"## Donut demo\n\nThis model is the result of fine-tuning 'VisionEncoderDecoderModel' on the naver-clova-ix/cord-v2 dataset.\n\nThe Weights and Biases report can be found here."
] | [
"TAGS\n#transformers #pytorch #vision-encoder-decoder #dataset-naver-clova-ix/cord-v2 #license-mit #endpoints_compatible #region-us \n",
"## Donut demo\n\nThis model is the result of fine-tuning 'VisionEncoderDecoderModel' on the naver-clova-ix/cord-v2 dataset.\n\nThe Weights and Biases report can be found here."... |
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. -->
# dat259-nor-wav2vec2
This model is a fine-tuned version of [NbAiLab/nb-wav2vec2-300m-nynorsk](https://huggingface.co/NbAiLab/nb-w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_8_0"], "model-index": [{"name": "dat259-nor-wav2vec2", "results": []}]} | Jethuestad/dat259-nor-wav2vec2 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_8_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T10:56:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
| dat259-nor-wav2vec2
===================
This model is a fine-tuned version of NbAiLab/nb-wav2vec2-300m-nynorsk on the common\_voice\_8\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 10.9446
* Wer: 1.1259
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_8_0 #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\... |
text-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. -->
# ady_classifier
This model is a fine-tuned version of [SZTAKI-HLT/hubert-base-cc](https://huggingface.co/SZTAKI-HLT/hubert-base-cc) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "ady_classifier", "results": []}]} | szabob-uly/ady_classifier | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T11:00:51+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ady_classifier
This model is a fine-tuned version of SZTAKI-HLT/hubert-base-cc on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information... | [
"# ady_classifier\n\nThis model is a fine-tuned version of SZTAKI-HLT/hubert-base-cc on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation da... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ady_classifier\n\nThis model is a fine-tuned version of SZTAKI-HLT/hubert-base-cc on an unknown dataset.\nIt achieves the following results on the e... |
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. -->
# t5-text2sql_v1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-text2sql_v1", "results": []}]} | mousaazari/t5-text2sql_v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T11:11:22+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-text2sql\_v1
===============
This model is a fine-tuned version of t5-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0772
* Rouge2 Precision: 0.8835
* Rouge2 Recall: 0.39
* Rouge2 Fmeasure: 0.5088
Model description
-----------------
More information needed
Inte... | [
"### 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: 30",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
translation | transformers |
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1259548205
- CO2 Emissions (in grams): 1938.8771
## Validation Metrics
- Loss: 0.685
- SacreBLEU: 57.231
- Gen len: 6.943 | {"language": ["en", "ar", "multilingual"], "tags": ["autotrain", "translation"], "datasets": ["alvations/autotrain-data-ara-transliterate"], "co2_eq_emissions": {"emissions": 1938.877077145461}} | alvations/autotrain-ara-transliterate-1259548205 | null | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain",
"translation",
"en",
"ar",
"multilingual",
"dataset:alvations/autotrain-data-ara-transliterate",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T11:23:51+00:00 | [] | [
"en",
"ar",
"multilingual"
] | TAGS
#transformers #pytorch #marian #text2text-generation #autotrain #translation #en #ar #multilingual #dataset-alvations/autotrain-data-ara-transliterate #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1259548205
- CO2 Emissions (in grams): 1938.8771
## Validation Metrics
- Loss: 0.685
- SacreBLEU: 57.231
- Gen len: 6.943 | [
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1259548205\n- CO2 Emissions (in grams): 1938.8771",
"## Validation Metrics\n\n- Loss: 0.685\n- SacreBLEU: 57.231\n- Gen len: 6.943"
] | [
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain #translation #en #ar #multilingual #dataset-alvations/autotrain-data-ara-transliterate #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Translation\n- Model ID: 1... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Number4/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T11:54:56+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text2text-generation | 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. -->
# bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-larger-summary
This model is a fine-tuned version of [google/mt5-small](htt... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-larger-summary", "results": []}]} | bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-larger-summary | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T12:46:20+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp-larger-summary
=========================================================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: nan
* Validation Lo... | [
"### 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': 5.6e-05, 'decay... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_opti... |
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-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "mnli... | Hamine/distilbert-base-uncased-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T12:46:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5486
* Accuracy: 0.8244
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-gc-indep
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbe... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-gc-indep", "results": []}]} | waynedsouza/distilbert-base-uncased-gc-indep | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T13:02:20+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-gc-indep
================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1014
* Accuracy: 0.983
* F1: 0.9746
Model description
-----------------
More information neede... | [
"### 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: 1",
"### 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 |
<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/1051826427837014017/v2TL... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/buffer-fastcompany-thinkwithgoogle | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T13:33:40+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Fast Company & Buffer & Think with Google
@buffer-fastcompany-thinkwithgoogle
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 devel... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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-newsroom-cnn-adam8bit-bs4x64acc_3
This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn-adam8bit-bs4x6... | {"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-newsroom-cnn-adam8bit-bs4x64acc_3", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type": "cnn_daily... | oMateos2020/pegasus-newsroom-cnn-adam8bit-bs4x64acc_3 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T13:39:58+00:00 | [] | [] | TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #model-index #autotrain_compatible #endpoints_compatible #region-us
| pegasus-newsroom-cnn-adam8bit-bs4x64acc\_3
==========================================
This model is a fine-tuned version of oMateos2020/pegasus-newsroom-cnn-adam8bit-bs4x64acc\_2 on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8566
* Rouge1: 44.268
* Rouge2: 21.5816... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6.4e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #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: 6.4e-05\n* train\... |
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. -->
# bart-v2-x
This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co/hyunwoongko/kobart) on the naem1023/... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["naem1023/aihub-speech"], "model-index": [{"name": "bart-v2-x", "results": []}]} | naem1023/bart-v2-speech | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:naem1023/aihub-speech",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T13:57:34+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-speech #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-v2-x
This model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-speech dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# bart-v2-x\n\nThis model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-speech dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedur... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-speech #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-v2-x\n\nThis model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-speech dataset.",
"## Model des... |
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... | zeptrus/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 | 2022-08-15T14:33:58+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: 0.0604
* Precision: 0.9277
* Recall: 0.9483
* F1: 0.9379
* Accuracy: 0.9865
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... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | sultanithree/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T14:34:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0626
* Precision: 0.9247
* Recall: 0.9341
* F1: 0.9294
* Accuracy: 0.9835
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* le... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Noura/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T14:45:17+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0625
* Precision: 0.9267
* Recall: 0.9359
* F1: 0.9313
* Accuracy: 0.9836
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* le... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | Eman222/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T14:58:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0611
* Precision: 0.9262
* Recall: 0.9361
* F1: 0.9311
* Accuracy: 0.9837
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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* le... |
text2text-generation | 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. -->
# test-summarization
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieve... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "test-summarization", "results": []}]} | wesbeaver/test-summarization | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T15:01:57+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| test-summarization
==================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.6449
* Validation Loss: 2.8528
* Epoch: 0
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': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
text-generation | transformers | ```
!pip install transformers
!pip install torch
```
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsToParagraphNeo1.3B")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsToParagraphNeo1.3B")
```
```
prompt = """
- ad... | {} | BigSalmon/PointsToParagraphNeo1.3B | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T15:07:32+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Example:
| [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | <h1>How to Write an informative Essay Perfectly?</h1>
<p>An informative essay is a paper that gives facts on a selected topic. In other words, it educates the reader approximately a particular issue, be it a term, occasion, natural phenomenon, etc. An informative essay doesn’t incorporate the writer’s evaluation or ana... | {} | JanetJWhitfield/essayreviews | null | [
"region:us"
] | null | 2022-08-15T15:09:02+00:00 | [] | [] | TAGS
#region-us
| <h1>How to Write an informative Essay Perfectly?</h1>
<p>An informative essay is a paper that gives facts on a selected topic. In other words, it educates the reader approximately a particular issue, be it a term, occasion, natural phenomenon, etc. An informative essay doesn’t incorporate the writer’s evaluation or ana... | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | ## Arabic MARBERT Poetry Classification Model
#### Model description
**arabic-MARBERT-poetry-classification Model** is a poetry classification model that was built by fine-tuning the [MARBERT](https://huggingface.co/UBC-NLP/MARBERT) model. For the fine-tuning, I used [APCD: Arabic Poem Comprehensive Dataset](https://hc... | {"language": ["ar"], "tags": ["text classification", "arabic", "poetry"], "widget": [{"text": "\u0642\u0650\u0641\u064e\u0627 \u0646\u064e\u0628\u0652\u0643\u0650 \u0645\u0650\u0646\u0652 \u0630\u0650\u0643\u0652\u0631\u064e\u0649 \u062d\u064e\u0628\u0650\u064a\u0628\u064d \u0648\u0645\u064e\u0646\u0652\u0632\u0650\u06... | Ammar-alhaj-ali/arabic-MARBERT-poetry-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text classification",
"arabic",
"poetry",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T15:20:26+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #text classification #arabic #poetry #ar #autotrain_compatible #endpoints_compatible #region-us
| ## Arabic MARBERT Poetry Classification Model
#### Model description
arabic-MARBERT-poetry-classification Model is a poetry classification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used APCD: Arabic Poem Comprehensive Dataset that includes 23 labels (البسيط,الطويل,الكامل,الوافر,الخفي... | [
"## Arabic MARBERT Poetry Classification Model",
"#### Model description\narabic-MARBERT-poetry-classification Model is a poetry classification model that was built by fine-tuning the MARBERT model. For the fine-tuning, I used APCD: Arabic Poem Comprehensive Dataset that includes 23 labels (البسيط,الطويل,الكامل,ا... | [
"TAGS\n#transformers #pytorch #bert #text-classification #text classification #arabic #poetry #ar #autotrain_compatible #endpoints_compatible #region-us \n",
"## Arabic MARBERT Poetry Classification Model",
"#### Model description\narabic-MARBERT-poetry-classification Model is a poetry classification model that... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="adil-o/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attri... | {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": ... | adil-o/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-15T15:50:05+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n \n ## Usage"
] |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | reachrkr/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-15T15:53:50+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
token-classification | transformers |
# Portuguese NER- TempClinBr - BioBERTpt(clin)
Treinado com BioBERTpt(clin), com o corpus TempClinBr.
Metricas:
```
precision recall f1-score support
0 1.00 0.85 0.92 33
1 0.73 0.69 0.71 78
2 0.75 0.55 ... | {"language": "pt", "datasets": ["TempClinBr"], "widget": [{"text": "Dispneia importante aos esfor\u00e7os + dor tipo peso no peito no esfor\u00e7o."}, {"text": "Obeso, has, icc c # cintilografia miocardica para avaliar angina. Discreto edema mmii pricn a esquerda."}, {"text": "Plastia Mitral ( Insuficiencia ), CRM Saf... | pucpr-br/tempclin-biobertpt-clin | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:TempClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T16:05:08+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us
|
# Portuguese NER- TempClinBr - BioBERTpt(clin)
Treinado com BioBERTpt(clin), com o corpus TempClinBr.
Metricas:
Parâmetros:
Eval no conjunto de teste - TempClinBr
OBS: Avaliação com tag "O" (label 7), se necessário fazer a média sem essa tag.
Como citar: em breve | [
"# Portuguese NER- TempClinBr - BioBERTpt(clin)\n\nTreinado com BioBERTpt(clin), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - TempClinBr\nOBS: Avaliação com tag \"O\" (label 7), se necessário fazer a média sem essa tag.\n\n\n\n\nComo citar: em breve"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese NER- TempClinBr - BioBERTpt(clin)\n\nTreinado com BioBERTpt(clin), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste -... |
text2text-generation | 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. -->
# summarizer-1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "summarizer-1", "results": []}]} | wesbeaver/test_model1 | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T16:06:24+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| summarizer-1
============
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 3.6364
* Validation Loss: 2.9054
* Epoch: 0
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': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamW... |
token-classification | transformers |
# Portuguese NER- TempClinBr - BioBERTpt(all)
Treinado com BioBERTpt(all), com o corpus TempClinBr.
Metricas:
```
precision recall f1-score support
0 0.75 0.90 0.82 291
1 0.77 1.00 0.87 33
2 1.00 0.25 0.40 ... | {"language": "pt", "datasets": ["TempClinBr"], "widget": [{"text": "Dispneia importante aos esfor\u00e7os + dor tipo peso no peito no esfor\u00e7o."}, {"text": "Obeso, has, icc c # cintilografia miocardica para avaliar angina. Discreto edema mmii pricn a esquerda."}, {"text": "Plastia Mitral ( Insuficiencia ), CRM Saf... | pucpr-br/tempclin-biobertpt-all | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:TempClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T16:37:28+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us
|
# Portuguese NER- TempClinBr - BioBERTpt(all)
Treinado com BioBERTpt(all), com o corpus TempClinBr.
Metricas:
Parâmetros:
Eval no conjunto de teste - TempClinBr
OBS: Avaliação com tag "O" (label 7), se necessário fazer a média sem essa tag.
Como citar: em breve | [
"# Portuguese NER- TempClinBr - BioBERTpt(all)\n\nTreinado com BioBERTpt(all), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - TempClinBr\nOBS: Avaliação com tag \"O\" (label 7), se necessário fazer a média sem essa tag.\n\n\n\n\nComo citar: em breve"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese NER- TempClinBr - BioBERTpt(all)\n\nTreinado com BioBERTpt(all), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - T... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | croumegous/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-15T16:43:16+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
token-classification | transformers |
# Portuguese NER- TempClinBr - BioBERTpt(bio)
Treinado com BioBERTpt(bio), com o corpus TempClinBr.
Metricas:
```
precision recall f1-score support
0 0.44 0.29 0.35 28
1 0.75 0.60 0.66 420
2 0.57 0.40 0.47 ... | {"language": "pt", "datasets": ["TempClinBr"], "widget": [{"text": "Dispneia importante aos esfor\u00e7os + dor tipo peso no peito no esfor\u00e7o."}, {"text": "Obeso, has, icc c # cintilografia miocardica para avaliar angina. Discreto edema mmii pricn a esquerda."}, {"text": "Plastia Mitral ( Insuficiencia ), CRM Saf... | pucpr-br/tempclin-biobertpt-bio | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"pt",
"dataset:TempClinBr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T16:43:28+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #bert #token-classification #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us
|
# Portuguese NER- TempClinBr - BioBERTpt(bio)
Treinado com BioBERTpt(bio), com o corpus TempClinBr.
Metricas:
Parâmetros:
Eval no conjunto de teste - TempClinBr
OBS: Avaliação com tag "O" (label 7), se necessário fazer a média sem essa tag.
Como citar: em breve | [
"# Portuguese NER- TempClinBr - BioBERTpt(bio)\n\nTreinado com BioBERTpt(bio), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - TempClinBr\nOBS: Avaliação com tag \"O\" (label 7), se necessário fazer a média sem essa tag.\n\n\n\n\nComo citar: em breve"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #pt #dataset-TempClinBr #autotrain_compatible #endpoints_compatible #region-us \n",
"# Portuguese NER- TempClinBr - BioBERTpt(bio)\n\nTreinado com BioBERTpt(bio), com o corpus TempClinBr.\n\nMetricas:\n\n\n\nParâmetros:\n\n\n\nEval no conjunto de teste - T... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="adil-o/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/... | adil-o/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-15T16:55:41+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
null | null | nnnnn | {} | EVERROCKET/test | null | [
"region:us"
] | null | 2022-08-15T18:03:53+00:00 | [] | [] | TAGS
#region-us
| nnnnn | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers | # NER-fine-tuned-BETO: model fine-tuned from BETO for NER task.
---
Language: es
Datasets:
- conll2002
- Babelscape/wikineural
## Introduction
[NER-fine-tuned-BETO] is a NER model that was fine-tuned from BETO on the 2002 Conll and the WikiNEuRal spanish datasets.
Model was trained on the Conll 2002 train dataset (~83... | {"language": "es", "license": "cc-by-4.0"} | NazaGara/NER-fine-tuned-BETO | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"es",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T18:17:58+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
| NER-fine-tuned-BETO: model fine-tuned from BETO for NER task.
=============================================================
---
Language: es
Datasets:
* conll2002
* Babelscape/wikineural
Introduction
------------
[NER-fine-tuned-BETO] is a NER model that was fine-tuned from BETO on the 2002 Conll and the Wi... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #es #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers | --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \ | {} | alishudi/distil_mse | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T18:29:21+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| --alpha_ce 0.0 --alpha_mlm 2.0 --alpha_cos 1.0 --alpha_act 1.0 --alpha_clm 0.0 --alpha_mse 0.0002 --mlm \ | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **QRDQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training fram... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFr... | rebolforces/qrdqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-08-15T18:29:39+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# QRDQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained ag... | [
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# QRDQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a QRDQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln68Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln68Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInforma... | {} | BigSalmon/InformalToFormalLincoln68Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T19:24:15+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Most likely outputs (Disclaimer: I highly recommend using this over just generating):
Keywords to sentences or sentence.
Infill / Infilling / Masking / Phrase Masking
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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... | Hagow/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T19:54:45+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0620
* Precision: 0.9373
* Recall: 0.9527
* F1: 0.9449
* Accuracy: 0.9867
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-botphilosophyq-chai_ste-marxhaunting-nsp_gpt2-shrekscriptlol-theofficialkeir-xannon199 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T20:17:37+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & ste & Ninja Sex Party but AI & Karl Marx & The Entire Shrek Scripts (COMPLETED) & Philosophy Quotes & Keir Kevlar & Xannon
@apesahoy-botphilosophyq-chai\_ste-marxhaunting-nsp\_gpt2-shrekscriptlol-theofficialkeir-xannon199
I was made with huggingtweets.
Create your own bot based on y... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #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. -->
# bert-small-finetuned-legal-contracts-larger4010
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["albertvillanova/legal_contracts"], "base_model": "google/bert_uncased_L-4_H-512_A-8", "model-index": [{"name": "bert-small-finetuned-legal-contracts-larger4010", "results": []}]} | muhtasham/bert-small-finetuned-legal-contracts-larger4010 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:albertvillanova/legal_contracts",
"base_model:google/bert_uncased_L-4_H-512_A-8",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T21:22:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-albertvillanova/legal_contracts #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert-small-finetuned-legal-contracts-larger4010
This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
The model was not trained on the whole dataset which is around 9.5 GB, but only
The first 40%... | [
"# bert-small-finetuned-legal-contracts-larger4010\n\nThis model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\nThe model was not trained on the whole dataset which is around 9.5 GB, but only... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-albertvillanova/legal_contracts #base_model-google/bert_uncased_L-4_H-512_A-8 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-small-finetuned-legal-contracts-larger4010... |
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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-chai_ste-nsp_gpt2-shrekscriptlol-theofficialkeir-xannon199/1660604291502/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-chai_ste-nsp_gpt2-shrekscriptlol-theofficialkeir-xannon199 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T21:56:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & ste & Ninja Sex Party but AI & Keir Kevlar & The Entire Shrek Scripts (COMPLETED) & Xannon
@apesahoy-chai\_ste-nsp\_gpt2-shrekscriptlol-theofficialkeir-xannon199
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1478805340212838413/YAJM... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nickjr/1660605150021/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nickjr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:12:08+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Nick Jr.
@nickjr
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.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1516077327981109259/Z4JJ... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nickelodeon/1660605723479/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nickelodeon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:18:13+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Nickelodeon
@nickelodeon
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.
Training data
--------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# diffusion_conditional
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/h... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "CelebA", "metrics": []} | shalpin87/diffusion_conditional | null | [
"diffusers",
"en",
"dataset:CelebA",
"license:apache-2.0",
"diffusers:DDPMConditionalPipeline",
"region:us"
] | null | 2022-08-15T22:23:16+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-CelebA #license-apache-2.0 #diffusers-DDPMConditionalPipeline #region-us
|
# diffusion_conditional
## Model description
This diffusion model is trained with the Diffusers library
on the 'CelebA' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe t... | [
"# diffusion_conditional",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'CelebA' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"## Trainin... | [
"TAGS\n#diffusers #en #dataset-CelebA #license-apache-2.0 #diffusers-DDPMConditionalPipeline #region-us \n",
"# diffusion_conditional",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'CelebA' dataset.",
"## Intended uses & limitations",
"#### How to use",
"##... |
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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-hannibalscript-nsp_gpt2-peepscript-shrekscriptlol-toywhole/1660605926582/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-hannibalscript-nsp_gpt2-peepscript-shrekscriptlol-toywhole | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:23:50+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & The Entire Shrek Scripts (COMPLETED) & Ninja Sex Party but AI & hannibal script & The Entire Toy Story 2 Script & Peep Show Script
@apesahoy-hannibalscript-nsp\_gpt2-peepscript-shrekscriptlol-toywhole
I was made with huggingtweets.
Create your own bot based on your favorite user wit... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | # KoMiniLM
🐣 Korean mini language model
## Overview
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language mod... | {} | shwan/KominiLM-steam_classifier3 | null | [
"arxiv:2002.10957",
"region:us"
] | null | 2022-08-15T22:24:15+00:00 | [
"2002.10957"
] | [] | TAGS
#arxiv-2002.10957 #region-us
| KoMiniLM
========
Korean mini language model
Overview
--------
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight kore... | [
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]](URL were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this project.",
"### Data sets",
"#... | [
"TAGS\n#arxiv-2002.10957 #region-us \n",
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]](URL were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case... |
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/1558615398651772929/3WnB... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/rocktwithapockt/1660606036832/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/rocktwithapockt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:26:53+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Rocket
@rocktwithapockt
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.
Training data
---------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | # KoMiniLM
🐣 Korean mini language model
## Overview
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight korean language mod... | {} | shwan/KominiLM-steam_classifier_readme_test | null | [
"pytorch",
"region:us"
] | null | 2022-08-15T22:27:01+00:00 | [] | [] | TAGS
#pytorch #region-us
| KoMiniLM
========
Korean mini language model
Overview
--------
Current language models usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and online serving in real-life applications due to latency and capacity constraints. In this project, we release a light weight kore... | [
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]] were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this project.",
"### Data sets",
"### C... | [
"TAGS\n#pytorch #region-us \n",
"### Object\n\n\nSelf-Attention Distribution and Self-Attention Value-Relation [[Wang et al., 2020]] were distilled from each discrete layer of the teacher model to the student model. Wang et al. distilled in the last layer of the transformer, but that was not the case in this proj... |
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-large-dataset-model-v3
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/goog... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-large-dataset-model-v3", "results": []}]} | mrgiraffe/vit-large-dataset-model-v3 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-15T22:37:16+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| vit-large-dataset-model-v3
==========================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0630
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_prec... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\... |
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/1400292170079293443/9cf8... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/risefallnickbck/1660606877518/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/risefallnickbck | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:40:58+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
TRAFON(s Backup Account)
@risefallnickbck
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.
Train... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1508543786737090570/k9hp... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/paramountplus/1660607189002/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/paramountplus | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:46:09+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Paramount+
@paramountplus
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.
Training data
-------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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/1196519479364268034/5Qpn... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/apesahoy-nsp_gpt2-peepscript-shrekscriptlol/1660607411241/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/apesahoy-nsp_gpt2-peepscript-shrekscriptlol | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-15T22:48:44+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Humongous Ape MP & Ninja Sex Party but AI & Peep Show Script & The Entire Shrek Scripts (COMPLETED)
@apesahoy-nsp\_gpt2-peepscript-shrekscriptlol
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 fol... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | tfhub |
## Model name: bert_en_cased_preprocess
## Description adapted from [TFHub](https://tfhub.dev/tensorflow/bert_en_cased_preprocess/3)
# Overview
This SavedModel is a companion of [BERT models](https://tfhub.dev/google/collections/bert/1) to preprocess plain text inputs into the input format expected by BERT. **Check ... | {"language": "en", "license": "apache-2.0", "library_name": "tfhub", "tags": ["text", "tokenizer", "preprocessor", "bert", "tensorflow"], "datasets": ["bookcorpus", "wikipedia"]} | Dimitre/bert_en_cased_preprocess | null | [
"tfhub",
"keras",
"text",
"tokenizer",
"preprocessor",
"bert",
"tensorflow",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-15T23:08:03+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#tfhub #keras #text #tokenizer #preprocessor #bert #tensorflow #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #has_space #region-us
|
## Model name: bert_en_cased_preprocess
## Description adapted from TFHub
# Overview
This SavedModel is a companion of BERT models to preprocess plain text inputs into the input format expected by BERT. Check the model documentation to find the correct preprocessing model for each particular BERT or other Transforme... | [
"## Model name: bert_en_cased_preprocess",
"## Description adapted from TFHub",
"# Overview\n\nThis SavedModel is a companion of BERT models to preprocess plain text inputs into the input format expected by BERT. Check the model documentation to find the correct preprocessing model for each particular BERT or o... | [
"TAGS\n#tfhub #keras #text #tokenizer #preprocessor #bert #tensorflow #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #has_space #region-us \n",
"## Model name: bert_en_cased_preprocess",
"## Description adapted from TFHub",
"# Overview\n\nThis SavedModel is a companion of BER... |
null | null | rival media | {} | Thuvo/Hoang | null | [
"region:us"
] | null | 2022-08-15T23:09:00+00:00 | [] | [] | TAGS
#region-us
| rival media | [] | [
"TAGS\n#region-us \n"
] |
null | tfhub |
## Model name: bert_en_cased_L-12_H-768_A-12
## Description adapted from [TFHub](https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/4)
# Overview
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural netwo... | {"language": "en", "license": "apache-2.0", "library_name": "tfhub", "tags": ["text", "bert", "tensorflow"], "datasets": ["bookcorpus", "wikipedia"]} | Dimitre/bert_en_cased_L-12_H-768_A-12 | null | [
"tfhub",
"keras",
"text",
"bert",
"tensorflow",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-08-15T23:09:47+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#tfhub #keras #text #bert #tensorflow #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #has_space #region-us
|
## Model name: bert_en_cased_L-12_H-768_A-12
## Description adapted from TFHub
# Overview
BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture. It was originally publishe... | [
"## Model name: bert_en_cased_L-12_H-768_A-12",
"## Description adapted from TFHub",
"# Overview\n\nBERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture. It was orig... | [
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"## Model name: bert_en_cased_L-12_H-768_A-12",
"## Description adapted from TFHub",
"# Overview\n\nBERT (Bidirectional Encoder Representations from Transfor... |
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/1397165180887445513/QGrN... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/pornosexualiza1/1660612833176/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/pornosexualiza1 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T00:19:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AI BOT
CRINGESEXUALISATION
@pornosexualiza1
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.
Training d... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Sparse BERT mini model (uncased)
Finetuned model pruned to 1:4 structured sparsity.
The model is a pruned version of the [BERT mini model](https://huggingface.co/prajjwal1/bert-mini).
## Intended Use
The model can be used for inference with sparsity optimization.
For further details on the model and its usage wil... | {"license": "mit"} | Intel/bert-mini-sst2-distilled-sparse-90-1X4-block | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T00:44:17+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Sparse BERT mini model (uncased)
================================
Finetuned model pruned to 1:4 structured sparsity.
The model is a pruned version of the BERT mini model.
Intended Use
------------
The model can be used for inference with sparsity optimization.
For further details on the model and its usage will b... | [] | [
"TAGS\n#transformers #pytorch #onnx #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #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. -->
# test-mlm
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "test-mlm", "results": []}]} | River-jh/bert-based-restaurant-review | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T00:46:17+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test-mlm
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2729
- Accuracy: 0.7100
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation dat... | [
"# test-mlm\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.2729\n- Accuracy: 0.7100",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test-mlm\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 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/1403256770848505857/cE9T... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/nomia2011/1660614778038/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/nomia2011 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T00:51:39+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
نومیا
@nomia2011
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.
Training data
-------------
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# Erlangshen-DeBERTa-v2-710M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLU任务，采用全词掩码的，中文版的7.1亿参数DeBERTa-v2-XLarge。
Good at solving NLU tasks, adopting Whole Word Masking, Chinese DeBERTa-v2-... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert"], "inference": true, "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese | null | [
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"fill-mask",
"bert",
"zh",
"arxiv:2209.02970",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-08-16T00:58:46+00:00 | [
"2209.02970"
] | [
"zh"
] | TAGS
#transformers #pytorch #safetensors #deberta-v2 #fill-mask #bert #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Erlangshen-DeBERTa-v2-710M-Chinese
==================================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
简介 Brief Introduction
---------------------
善于处理NLU任务，采用全词掩码的，中文版的7.1亿参数DeBERTa-v2-XLarge。
Good at solving NLU tasks, adopting Whole Word Masking, Chinese DeBERTa-v2-XLarge with 710M paramete... | [
"### 下游任务 Performance\n\n\n我们展示了下列下游任务的结果：\n\n\nWe present the results on the following tasks:\n\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 cite the our paper:\n\n\n也可以引用我们的网站:\n\n\nYou can also cite our website:"
... | [
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #fill-mask #bert #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### 下游任务 Performance\n\n\n我们展示了下列下游任务的结果：\n\n\nWe present the results on the following tasks:\n\n\n\n使用 Usage\n--------\n\n\n引用 ... |
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/1479995491651833867/duT0... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/hordemommy/1660617228404/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/hordemommy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T01:33:03+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Average Hyperstition Enjoyer
@hordemommy
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.
Traini... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
image-classification | transformers |
# Electric-Car-Brand-Classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.co... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | zjs81/Electric-Car-Brand-Classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T02:45:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# Electric-Car-Brand-Classifier
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### BMW Electric Car
!BMW Electric Car
#### Chevrolet Electric Car
!Chevrolet Electric Car
... | [
"# Electric-Car-Brand-Classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### BMW Electric Car\n\n!BMW Electric Car",
"#### Chevrolet Electric Car\n\n... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# Electric-Car-Brand-Classifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nR... |
audio-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. -->
# wav2vec2-base-ks-linear_lrX100
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-linear_lrX100", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-linear_lrX100 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T03:10:38+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-linear\_lrX100
===============================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6970
* Accuracy: 0.8001
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
fill-mask | transformers |
# bert-tiny-finetuned-legal-contracts-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/google/google/bert_uncased_L-4_H-512_A-8) on the portion of legal_contracts dataset for 1 epoch.
# Note
The model was not trained on the whole dataset which is around ... | {"datasets": ["albertvillanova/legal_contracts"]} | muhtasham/bert-small-finetuned-legal-contracts-larger20-5-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"dataset:albertvillanova/legal_contracts",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T03:33:53+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #dataset-albertvillanova/legal_contracts #autotrain_compatible #endpoints_compatible #region-us
|
# bert-tiny-finetuned-legal-contracts-longer
This model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the portion of legal_contracts dataset for 1 epoch.
# Note
The model was not trained on the whole dataset which is around 9.5 GB, but only
## The first 20% of 'train' + the last 5% of 'train'.
... | [
"# bert-tiny-finetuned-legal-contracts-longer\n\nThis model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the portion of legal_contracts dataset for 1 epoch.",
"# Note \nThe model was not trained on the whole dataset which is around 9.5 GB, but only",
"## The first 20% of 'train' + the last 5%... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #dataset-albertvillanova/legal_contracts #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-tiny-finetuned-legal-contracts-longer\n\nThis model is a fine-tuned version of google/bert_uncased_L-4_H-512_A-8 on the portion of legal_contract... |
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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](ht... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type":... | racheltong/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T03:48:50+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0665
* Accuracy: 0.9785
Model description
------... | [
"### 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 #swin #image-classification #generated_from_trainer #dataset-imagefolder #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* learni... |
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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": []}]} | jamie613/distilbert-base-uncased-finetuned-clinc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T04:09:30+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7710
* Accuracy: 0.9177
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 48\n* eval\\_batch\\_size: 48\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #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... |
audio-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. -->
# wav2vec2-base-ks-linear_lrX10
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-linear_lrX10", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-linear_lrX10 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T04:14:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-linear\_lrX10
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0471
* Accuracy: 0.6686
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and eps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
null | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-celeb-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingf... | {"language": "en", "license": "apache-2.0", "library_name": "diffusers", "tags": [], "datasets": "data/img_align_celeba", "metrics": []} | rdruce/ddpm-celeb-128 | null | [
"diffusers",
"en",
"dataset:data/img_align_celeba",
"license:apache-2.0",
"diffusers:UNet2DModel",
"region:us"
] | null | 2022-08-16T04:20:08+00:00 | [] | [
"en"
] | TAGS
#diffusers #en #dataset-data/img_align_celeba #license-apache-2.0 #diffusers-UNet2DModel #region-us
|
# ddpm-celeb-128
## Model description
This diffusion model is trained with the Diffusers library
on the 'data/img_align_celeba' dataset.
## Intended uses & limitations
#### How to use
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: de... | [
"# ddpm-celeb-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'data/img_align_celeba' dataset.",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]",
"##... | [
"TAGS\n#diffusers #en #dataset-data/img_align_celeba #license-apache-2.0 #diffusers-UNet2DModel #region-us \n",
"# ddpm-celeb-128",
"## Model description\n\nThis diffusion model is trained with the Diffusers library \non the 'data/img_align_celeba' dataset.",
"## Intended uses & limitations",
"#### How to ... |
question-answering | 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. -->
# en_QA_2_epochs
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "en_QA_2_epochs", "results": []}]} | Mostafa3zazi/en_QA_2_epochs | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T04:45:12+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
|
# en_QA_2_epochs
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# en_QA_2_epochs\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"# en_QA_2_epochs\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model desc... |
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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xtreme"], "metrics": ["f1"], "model-index": [{"name": "xlm-roberta-base-finetuned-panx-de", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "xtreme", "type": "xtreme", "config": "PAN-X.de", "s... | hhffxx/xlm-roberta-base-finetuned-panx-de | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T05:00:29+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-panx-de
==================================
This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3089
* F1: 0.8217
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #dataset-xtreme #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
reinforcement-learning | null |
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jasheershihab/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False e... | {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.48 +/... | jasheershihab/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2022-08-16T05:31:57+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n \n ## Usage"
] |
audio-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. -->
# wav2vec2-base-ks-linear_lrX1000
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-linear_lrX1000", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-linear_lrX1000 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T05:47:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-linear\_lrX1000
================================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5661
* Accuracy: 0.8325
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.03\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
null | null | Models for https://github.com/k2-fsa/icefall/pull/529 | {"license": "apache-2.0"} | wangtiance/lightweight_ctc | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-08-16T06:03:24+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| Models for URL | [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
audio-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. -->
# wav2vec2-base-ks-padpt200
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-ks-padpt200", "results": []}]} | Jungwoo4021/wav2vec2-base-ks-padpt200 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-08-16T06:40:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| wav2vec2-base-ks-padpt200
=========================
This model is a fine-tuned version of facebook/wav2vec2-base on the superb dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6540
* Accuracy: 0.6037
Model description
-----------------
More information needed
Intended uses & limitat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 0\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #text-classification #audio-classification #generated_from_trainer #dataset-superb #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*... |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-smal... | {"license": "apache-2.0", "tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "mt5-small-finetuned-amazon-en-es", "results": []}]} | chisun/mt5-small-finetuned-amazon-en-es | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-08-16T06:50:24+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-amazon-en-es
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6642
* Rouge1: 12.9097
* Rouge2: 3.2756
* Rougel: 12.2885
* Rougelsum: 12.3186
Model description
--------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 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: 8",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #summarization #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n*... |
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