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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text-to-speech | espnet | ## ESPnet2 TTS pretrained model
### `kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest`
♻️ Imported from https://zenodo.org/record/5521446/
This model was trained by kan-bayashi using tsukuyomi/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to ... | {"language": "ja", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["tsukuyomi"]} | espnet/kan-bayashi_tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest | null | [
"espnet",
"audio",
"text-to-speech",
"ja",
"dataset:tsukuyomi",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"ja"
] | TAGS
#espnet #audio #text-to-speech #ja #dataset-tsukuyomi #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 TTS pretrained model
### 'kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest'
️ Imported from URL
This model was trained by kan-bayashi using tsukuyomi/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using tsukuyomi/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
... | [
"TAGS\n#espnet #audio #text-to-speech #ja #dataset-tsukuyomi #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/tsukuyomi_tts_finetune_full_band_jsut_vits_raw_phn_jaconv_pyopenjtalk_prosody_latest'\n️ Imported from URL\n\nThis model was trained by kan-bayas... |
text-to-speech | espnet | ## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_full_band_multi_spk_vits`
♻️ Imported from https://zenodo.org/record/5521431/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_full_band_multi_spk_vits | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 TTS pretrained model
### 'kan-bayashi/vctk_full_band_multi_spk_vits'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_full_band_multi_spk_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_full_band_multi_spk_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to u... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_conformer_fastspeech2`
♻️ Imported from https://zenodo.org/record/4036264/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
`... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_conformer_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst_conformer_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #has_space #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo:... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_fastspeech`
♻️ Imported from https://zenodo.org/record/3986241/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@i... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_fastspeech | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst_fastspeech'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_fastspeech'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_fastspeech2`
♻️ Imported from https://zenodo.org/record/4036266/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_tacotron2`
♻️ Imported from https://zenodo.org/record/3986237/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@in... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst_tacotron2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2"... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst_transformer`
♻️ Imported from https://zenodo.org/record/4037456/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_transformer | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst_transformer'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst+xvector_conformer_fastspeech2`
♻️ Imported from https://zenodo.org/record/4394608/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_xvector_conformer_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst+xvector_conformer_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst+xvector_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst+xvector_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: Ho... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst+xvector_tacotron2`
♻️ Imported from https://zenodo.org/record/4394598/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```Bi... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_xvector_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst+xvector_tacotron2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst+xvector_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst+xvector_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_gst+xvector_transformer`
♻️ Imported from https://zenodo.org/record/4393277/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_gst_xvector_transformer | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_gst+xvector_transformer'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst+xvector_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_gst+xvector_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use i... |
text-to-speech | espnet | ## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_multi_spk_vits`
♻️ Imported from https://zenodo.org/record/5500759/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_multi_spk_vits | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 TTS pretrained model
### 'kan-bayashi/vctk_multi_spk_vits'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_multi_spk_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_multi_spk_vits'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPn... |
text-to-speech | espnet | ## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5521431/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: Ho... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g-truncated-50b003 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 TTS pretrained model
### 'kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arX... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_tts_train_full_band_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4036264/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_-truncated-69081b | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_conformer_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using ... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4036266/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_fastspeech2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 ... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best`
♻️ Imported from https://zenodo.org/record/3986241/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.loss.best | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_fastspeech_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 r... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best`
♻️ Imported from https://zenodo.org/record/3986237/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.best | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 re... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4037456/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst_transformer_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 ... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4394608/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/esp... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_xvector_conformer_fastspeech2_transform-truncated-e051a9 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ES... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was t... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_gst+xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4394598/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in E... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_gst_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_gst+xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst+xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_gst+xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk... |
text-to-speech | espnet | ## ESPnet2 TTS pretrained model
### `kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave`
♻️ Imported from https://zenodo.org/record/5500759/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use i... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 TTS pretrained model
### 'kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 TTS pretrained model",
"### 'kan-bayashi/vctk_tts_train_multi_spk_vits_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave'\n️ Imported from URL\n\nThis model was trained by kan-bayashi us... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4394602/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_xvector_conformer_fastspeech2_transformer_t-truncated-69a657 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was train... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4394600/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPne... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.loss.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_xvector_tacotron2_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave`
♻️ Imported from https://zenodo.org/record/4393279/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESP... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.loss.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.URL'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_tts_train_xvector_transformer_raw_phn_tacotron_g2p_en_no_space_train.URL'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/t... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_xvector_conformer_fastspeech2`
♻️ Imported from https://zenodo.org/record/4394602/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPn... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_xvector_conformer_fastspeech2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_xvector_conformer_fastspeech2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_xvector_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_xvector_conformer_fastspeech2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_xvector_tacotron2`
♻️ Imported from https://zenodo.org/record/4394600/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_xvector_tacotron2 | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_xvector_tacotron2'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_xvector_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_xvector_tacotron2'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPn... |
text-to-speech | espnet | ## Example ESPnet2 TTS model
### `kan-bayashi/vctk_xvector_transformer`
♻️ Imported from https://zenodo.org/record/4393279/
This model was trained by kan-bayashi using vctk/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibT... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["vctk"]} | espnet/kan-bayashi_vctk_xvector_transformer | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:vctk",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## Example ESPnet2 TTS model
### 'kan-bayashi/vctk_xvector_transformer'
️ Imported from URL
This model was trained by kan-bayashi using vctk/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_xvector_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-vctk #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 TTS model",
"### 'kan-bayashi/vctk_xvector_transformer'\n️ Imported from URL\n\nThis model was trained by kan-bayashi using vctk/tts1 recipe in espnet.",
"### Demo: How to use in ES... |
text-to-speech | espnet |
# ESPnet2 ASR pretrained model
## `kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave`
♻️ Imported from <https://zenodo.org/record/4017026#.YN70XJozZH4>
This model was trained by kan-bayashi using ljspeech/tts1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo:... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ljspeech"], "widget": [{"text": "Hello, how are you doing?"}]} | espnet/kan_bayashi_jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.loss.ave | null | [
"espnet",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
# ESPnet2 ASR pretrained model
## 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'
️ Imported from <URL
This model was trained by kan-bayashi using ljspeech/tts1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
### Training config
See fu... | [
"# ESPnet2 ASR pretrained model",
"## 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n\n️ Imported from <URL\n\nThis model was trained by kan-bayashi using ljspeech/tts1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:",
"###... | [
"TAGS\n#espnet #audio #text-to-speech #en #dataset-ljspeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ASR pretrained model",
"## 'kan-bayashi/jsut_tts_train_conformer_fastspeech2_raw_phn_jaconv_pyopenjtalk_train.URL'\n\n️ Imported from <URL\n\nThis model was trained by kan-bayashi using lj... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char`
This model was trained by Pengcheng Guo using wenetspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 5c21f63e45e0961a5d817017c282b0cafd68a3aa
pip install... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["wenetspeech"]} | espnet/pengcheng_guo_wenetspeech_asr_train_asr_raw_zh_char | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:wenetspeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-wenetspeech #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/pengcheng\_guo\_wenetspeech\_asr\_train\_asr\_raw\_zh\_char'
This model was trained by Pengcheng Guo using wenetspeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Wed Oct 6 15:11:20 CST 2021'
* python ve... | [
"### 'espnet/pengcheng\\_guo\\_wenetspeech\\_asr\\_train\\_asr\\_raw\\_zh\\_char'\n\n\nThis model was trained by Pengcheng Guo using wenetspeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Wed Oct 6 15:11:20 CST 2021'\n* python version: ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-wenetspeech #license-cc-by-4.0 #region-us \n",
"### 'espnet/pengcheng\\_guo\\_wenetspeech\\_asr\\_train\\_asr\\_raw\\_zh\\_char'\n\n\nThis model was trained by Pengcheng Guo using wenetspeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\... |
null | espnet |
## ESPnet2 ASR model
### `espnet/roshansh_how2_asr_raw_ft_sum_valid.acc`
This model was trained by roshansh-cmu using how2 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout e6f42a9783a5d9eba0687c19417f933e890722d7
pip install -e .
cd egs2/how2/su... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-summarization"], "datasets": ["how2"]} | espnet/roshansh_how2_asr_raw_ft_sum_valid.acc | null | [
"espnet",
"audio",
"automatic-speech-summarization",
"en",
"dataset:how2",
"arxiv:2110.06263",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.06263",
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-summarization #en #dataset-how2 #arxiv-2110.06263 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/roshansh\_how2\_asr\_raw\_ft\_sum\_valid.acc'
This model was trained by roshansh-cmu using how2 recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Mon Feb 7 15:24:21 EST 2022'
* python version: '3.8.12 (default... | [
"### 'espnet/roshansh\\_how2\\_asr\\_raw\\_ft\\_sum\\_valid.acc'\n\n\nThis model was trained by roshansh-cmu using how2 recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Mon Feb 7 15:24:21 EST 2022'\n* python version: '3.8.12 (default, Oct 12 ... | [
"TAGS\n#espnet #audio #automatic-speech-summarization #en #dataset-how2 #arxiv-2110.06263 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/roshansh\\_how2\\_asr\\_raw\\_ft\\_sum\\_valid.acc'\n\n\nThis model was trained by roshansh-cmu using how2 recipe in espnet.",
"### Demo: How to use in ESPn... |
automatic-speech-recognition | espnet |
# ESPnet2 ASR pretrained model
## `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best, fs=16k, lang=en`
♻️ Imported from <https://zenodo.org/record/3966501#.YOAOUZozZH5>
This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/)... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"], "inference": false} | espnet/shinji-watanabe-librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #license-cc-by-4.0 #region-us
|
# ESPnet2 ASR pretrained model
## 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL, fs=16k, lang=en'
️ Imported from <URL
This model was trained by Shinji Watanabe using librispeech recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training config
... | [
"# ESPnet2 ASR pretrained model",
"## 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL, fs=16k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ASR pretrained model",
"## 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL, fs=16k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by Shinji Wata... |
automatic-speech-recognition | espnet | ## ESPnet2 SLU pretrained model
### `siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`
♻️ Imported from https://zenodo.org/record/5590204
This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
`... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc"]} | espnet/siddhana_fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-fsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 SLU pretrained model
### 'siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best'
️ Imported from URL
This model was trained by siddhana using fsc/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 SLU pretrained model",
"### 'siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best'\n️ Imported from URL\n\nThis model was trained by siddhana using fsc/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-fsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 SLU pretrained model",
"### 'siddhana/fsc_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best'\n️ Imported from URL\n\nThis model was trained by siddhan... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `siddhana/fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best`
♻️ Imported from https://zenodo.org/record/5656007
This model was trained by siddhana using fsc_challenge/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc_challenge"]} | espnet/siddhana_fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_r-truncated-36174d | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc_challenge",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-fsc_challenge #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'siddhana/fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best'
️ Imported from URL
This model was trained by siddhana using fsc_challenge/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'siddhana/fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best'\n️ Imported from URL\n\nThis model was trained by siddhana using fsc_challenge/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arX... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-fsc_challenge #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'siddhana/fsc_challenge_asr_train_asr_hubert_transformer_adam_specaug_raw_en_word_valid.acc.ave_5best'\n️ Imported from URL\n\nThis model wa... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best`
♻️ Imported from https://zenodo.org/record/5655832
This model was trained by siddhana using fsc_unseen/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc_unseen"]} | espnet/siddhana_fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_fine-truncated-ef9dab | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc_unseen",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-fsc_unseen #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best'
️ Imported from URL
This model was trained by siddhana using fsc_unseen/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best'\n️ Imported from URL\n\nThis model was trained by siddhana using fsc_unseen/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-fsc_unseen #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best'\n️ Imported from URL\n\nThis model... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`
This model was trained by Siddhant using slue-voxceleb recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 17758ad804fd7c4b6f88ef5601f475a241dc... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slue-voxceleb"]} | espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slue-voxceleb",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-slue-voxceleb #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/siddhana\_slue\_asr\_train\_asr\_conformer\_raw\_en\_word\_valid.acc.ave\_10best'
This model was trained by Siddhant using slue-voxceleb recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Dec 28 12:28:28 ES... | [
"### 'espnet/siddhana\\_slue\\_asr\\_train\\_asr\\_conformer\\_raw\\_en\\_word\\_valid.acc.ave\\_10best'\n\n\nThis model was trained by Siddhant using slue-voxceleb recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Dec 28 12:28:28 EST 2021... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-slue-voxceleb #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/siddhana\\_slue\\_asr\\_train\\_asr\\_conformer\\_raw\\_en\\_word\\_valid.acc.ave\\_10best'\n\n\nThis model was trained by Siddhant using slue-voxceleb recipe in espnet.... |
automatic-speech-recognition | espnet | ## ESPnet2 SLU (Entity Classification) pretrained model
### `siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`
♻️ Imported from https://zenodo.org/record/5590204
This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use ... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["fsc"]} | espnet/siddhana_slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:fsc",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-fsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 SLU (Entity Classification) pretrained model
### 'siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best'
️ Imported from URL
This model was trained by siddhana using fsc/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 SLU (Entity Classification) pretrained model",
"### 'siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best'\n️ Imported from URL\n\nThis model was trained by siddhana using fsc/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-fsc #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 SLU (Entity Classification) pretrained model",
"### 'siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best'\n️ Imported from URL\n\nThis model was traine... |
automatic-speech-recognition | espnet | ## ESPnet2 SLU pretrained model
### `siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best`
♻️ Imported from https://zenodo.org/record/5590384
This model was trained by siddhana using slurp/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# co... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["slurp"]} | espnet/siddhana_slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:slurp",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-slurp #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 SLU pretrained model
### 'siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best'
️ Imported from URL
This model was trained by siddhana using slurp/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 SLU pretrained model",
"### 'siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best'\n️ Imported from URL\n\nThis model was trained by siddhana using slurp/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-slurp #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 SLU pretrained model",
"### 'siddhana/slurp_new_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best'\n️ Imported from URL\n\nThis model was trained by siddhana using slurp... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp`
This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b0ff60946ada6753af7942... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR model
### 'espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp'
This model was trained by simpleoier using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
## ASR config
<details><summary>expand</summary>
</details>
### Citing ESPnet
... | [
"## ESPnet2 ASR model",
"### 'espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp'\n\nThis model was trained by simpleoier using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",
... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR model",
"### 'espnet/simpleoier_librispeech_asr_train_asr_conformer7_hubert_ll60k_large_raw_en_bpe5000_sp'\n\nThis model was trained by simpleoier using librispeech r... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp`
This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b0ff60946ada6753af79... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## ESPnet2 ASR model
### 'espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp'
This model was trained by simpleoier using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
## ASR config
<details><summary>expand</summary>
</details>
### Citing ESPnet... | [
"## ESPnet2 ASR model",
"### 'espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp'\n\nThis model was trained by simpleoier using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"## ASR config\n\n<details><summary>expand</summary>\n\n\n\n</details>",... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR model",
"### 'espnet/simpleoier_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp'\n\nThis model was trained by simpleoier using librispeech... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp`
This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b0ff60946ada6753af79423a2e606... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/simpleoier\_librispeech\_asr\_train\_asr\_conformer7\_wavlm\_large\_raw\_en\_bpe5000\_sp'
This model was trained by simpleoier using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Tue Jan 4 20:5... | [
"### 'espnet/simpleoier\\_librispeech\\_asr\\_train\\_asr\\_conformer7\\_wavlm\\_large\\_raw\\_en\\_bpe5000\\_sp'\n\n\nThis model was trained by simpleoier using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Tue Jan 4 20:52:48 ... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"### 'espnet/simpleoier\\_librispeech\\_asr\\_train\\_asr\\_conformer7\\_wavlm\\_large\\_raw\\_en\\_bpe5000\\_sp'\n\n\nThis model was trained by simpleoier using librispeech recipe in ... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `su_openslr36`
♻️ Imported from https://zenodo.org/record/5090135/
This model was trained by su_openslr36 using su_openslr36/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inprocee... | {"language": "su", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["su_openslr36"]} | espnet/su_openslr36 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"su",
"dataset:su_openslr36",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"su"
] | TAGS
#espnet #audio #automatic-speech-recognition #su #dataset-su_openslr36 #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'su_openslr36'
️ Imported from URL
This model was trained by su_openslr36 using su_openslr36/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'su_openslr36'\n️ Imported from URL\n\nThis model was trained by su_openslr36 using su_openslr36/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #su #dataset-su_openslr36 #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'su_openslr36'\n️ Imported from URL\n\nThis model was trained by su_openslr36 using su_openslr36/asr1 recipe in espnet.",
"### Demo: How to... |
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `espnet/sujay_catslu_map`
This model was trained by Sujay S Kumar using catslu recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout e31965d55993766461f0964216a0bb9aea3cfb7a
pip install -e .
cd egs2/catslu/asr1
./run.sh --ski... | {"language": "zh", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["catslu"]} | espnet/sujay_catslu_map | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"zh",
"dataset:catslu",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#espnet #audio #automatic-speech-recognition #zh #dataset-catslu #license-cc-by-4.0 #region-us
| ESPnet2 ASR model
-----------------
### 'espnet/sujay\_catslu\_map'
This model was trained by Sujay S Kumar using catslu recipe in espnet.
### Demo: How to use in ESPnet2
RESULTS
=======
Environments
------------
* date: 'Sun Oct 3 12:53:16 EDT 2021'
* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) ... | [
"### 'espnet/sujay\\_catslu\\_map'\n\n\nThis model was trained by Sujay S Kumar using catslu recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n* date: 'Sun Oct 3 12:53:16 EDT 2021'\n* python version: '3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]'\... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #zh #dataset-catslu #license-cc-by-4.0 #region-us \n",
"### 'espnet/sujay\\_catslu\\_map'\n\n\nThis model was trained by Sujay S Kumar using catslu recipe in espnet.",
"### Demo: How to use in ESPnet2\n\n\nRESULTS\n=======\n\n\nEnvironments\n------------\n\n\n... |
automatic-speech-recognition | espnet | ## ESPnet2 ASR pretrained model
### `https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1`
♻️ Imported from https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1
This model was trained by vectominist using seame/asr1 recipe in [espnet](https://github.co... | {"language": ["en", "zh", "multilingual"], "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["seame"]} | espnet/vectominist_seame_asr_conformer_bpe5626 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"zh",
"multilingual",
"dataset:seame",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en",
"zh",
"multilingual"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #zh #multilingual #dataset-seame #arxiv-1804.00015 #license-cc-by-4.0 #region-us
| ## ESPnet2 ASR pretrained model
### 'URL
️ Imported from URL
This model was trained by vectominist using seame/asr1 recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## ESPnet2 ASR pretrained model",
"### 'URL\n️ Imported from URL\n\nThis model was trained by vectominist using seame/asr1 recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #zh #multilingual #dataset-seame #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## ESPnet2 ASR pretrained model",
"### 'URL\n️ Imported from URL\n\nThis model was trained by vectominist using seame/asr1 recipe in espnet.",
"### Demo: How to use in... |
automatic-speech-recognition | espnet |
# ESPnet2 ASR pretrained model
## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en`
This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See https://gi... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"], "inference": false} | espnet/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #license-cc-by-4.0 #region-us
|
# ESPnet2 ASR pretrained model
## 'Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en'
This model was trained by Takashi Maekaku using librispeech recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training... | [
"# ESPnet2 ASR pretrained model",
"## 'Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en'\n\nThis model was trained by Takashi Maekaku using librispeech recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Resul... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ASR pretrained model",
"## 'Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_hubert_960hr_large_raw_en_bpe5000_sp_26epoch, fs=16k, lang=en'\n\nThis model was trained by Taka... |
automatic-speech-recognition | espnet |
# ESPnet2 ASR pretrained model
## `Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en`
This model was trained by Takashi Maekaku using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See https://... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"], "inference": false} | espnet/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_larg-truncated-5b94d9 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #license-cc-by-4.0 #region-us
|
# ESPnet2 ASR pretrained model
## 'Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en'
This model was trained by Takashi Maekaku using librispeech recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Traini... | [
"# ESPnet2 ASR pretrained model",
"## 'Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en'\n\nThis model was trained by Takashi Maekaku using librispeech recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Res... | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ASR pretrained model",
"## 'Xuankai Chang/xuankai_chang_librispeech_asr_train_asr_conformer7_wav2vec2_960hr_large_raw_en_bpe5000_sp_25epoch, fs=16k, lang=en'\n\nThis model was trained by Ta... |
audio-to-audio | espnet |
# ESPnet2 ENH pretrained model
## `neillu23/dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best, fs=16k, lang=en`
♻️ Imported from <https://zenodo.org/record/4923697#.YOAOIpozZH4>.
This model was trained by neillu23 using dns_ins20 recipe in [espnet](https://github.com/espnet/espnet/).
### Python API
```text
See... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "audio-source-separation", "audio-to-audio"], "datasets": ["dns_ins20"], "inference": false} | espnet/yen-ju-lu-dns_ins20_enh_train_enh_blstm_tf_raw_valid.loss.best | null | [
"espnet",
"audio",
"audio-source-separation",
"audio-to-audio",
"en",
"dataset:dns_ins20",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-dns_ins20 #license-cc-by-4.0 #region-us
|
# ESPnet2 ENH pretrained model
## 'neillu23/dns_ins20_enh_train_enh_blstm_tf_raw_valid.URL, fs=16k, lang=en'
️ Imported from <URL
This model was trained by neillu23 using dns_ins20 recipe in espnet.
### Python API
### Evaluate in the recipe
### Results
### Training config
See full config in 'URL'
| [
"# ESPnet2 ENH pretrained model",
"## 'neillu23/dns_ins20_enh_train_enh_blstm_tf_raw_valid.URL, fs=16k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by neillu23 using dns_ins20 recipe in espnet.",
"### Python API",
"### Evaluate in the recipe",
"### Results",
"### Training config\n\nSee full... | [
"TAGS\n#espnet #audio #audio-source-separation #audio-to-audio #en #dataset-dns_ins20 #license-cc-by-4.0 #region-us \n",
"# ESPnet2 ENH pretrained model",
"## 'neillu23/dns_ins20_enh_train_enh_blstm_tf_raw_valid.URL, fs=16k, lang=en'\n\n️ Imported from <URL\n\nThis model was trained by neillu23 using dns_ins20 ... |
text-generation | null |
# Bot Edan | {"tags": ["conversational"]} | estehpanas/pascalbot | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Bot Edan | [
"# Bot Edan"
] | [
"TAGS\n#conversational #region-us \n",
"# Bot Edan"
] |
question-answering | transformers |
# camembert-base-squadFR-fquad-piaf
## Description
Question-answering French model, using base [CamemBERT](https://camembert-model.fr/) fine-tuned on a combo of three French Q&A datasets:
1. [PIAFv1.1](https://www.data.gouv.fr/en/datasets/piaf-le-dataset-francophone-de-questions-reponses/)
2. [FQuADv1.0](https://fq... | {"language": "fr", "datasets": ["piaf", "FQuAD", "SQuAD-FR"], "widget": [{"text": "Comment s'appelle le portail open data du gouvernement ?", "context": "Etalab est une administration publique fran\u00e7aise qui fait notamment office de Chief Data Officer de l'\u00c9tat et coordonne la conception et la mise en \u0153uv... | AgentPublic/camembert-base-squadFR-fquad-piaf | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"camembert",
"question-answering",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #safetensors #camembert #question-answering #fr #dataset-piaf #dataset-FQuAD #dataset-SQuAD-FR #endpoints_compatible #region-us
|
# camembert-base-squadFR-fquad-piaf
## Description
Question-answering French model, using base CamemBERT fine-tuned on a combo of three French Q&A datasets:
1. PIAFv1.1
2. FQuADv1.0
3. SQuAD-FR (SQuAD automatically translated to French)
## Training hyperparameters
## Evaluation results
### FQuAD v1.0 Evaluatio... | [
"# camembert-base-squadFR-fquad-piaf",
"## Description\n\nQuestion-answering French model, using base CamemBERT fine-tuned on a combo of three French Q&A datasets:\n\n1. PIAFv1.1\n2. FQuADv1.0\n3. SQuAD-FR (SQuAD automatically translated to French)",
"## Training hyperparameters",
"## Evaluation results",
"... | [
"TAGS\n#transformers #pytorch #tf #safetensors #camembert #question-answering #fr #dataset-piaf #dataset-FQuAD #dataset-SQuAD-FR #endpoints_compatible #region-us \n",
"# camembert-base-squadFR-fquad-piaf",
"## Description\n\nQuestion-answering French model, using base CamemBERT fine-tuned on a combo of three Fr... |
null | transformers |
# dpr-ctx_encoder-fr_qa-camembert
## Description
French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A
## Data
### French Q&A
We use a combination of three French Q&A datasets:
1. [PIAFv1.1](https://www.... | {"language": "fr", "datasets": ["piaf", "FQuAD", "SQuAD-FR"]} | AgentPublic/dpr-ctx_encoder-fr_qa-camembert | null | [
"transformers",
"pytorch",
"camembert",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"arxiv:2004.04906",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #fr #dataset-piaf #dataset-FQuAD #dataset-SQuAD-FR #arxiv-2004.04906 #arxiv-1911.03894 #endpoints_compatible #region-us
|
# dpr-ctx_encoder-fr_qa-camembert
## Description
French DPR model using CamemBERT as base and then fine-tuned on a combo of three French Q&A
## Data
### French Q&A
We use a combination of three French Q&A datasets:
1. PIAFv1.1
2. FQuADv1.0
3. SQuAD-FR (SQuAD automatically translated to French)
### Training
We... | [
"# dpr-ctx_encoder-fr_qa-camembert",
"## Description\n\nFrench DPR model using CamemBERT as base and then fine-tuned on a combo of three French Q&A",
"## Data",
"### French Q&A \nWe use a combination of three French Q&A datasets: \n\n1. PIAFv1.1\n2. FQuADv1.0\n3. SQuAD-FR (SQuAD automatically translated to Fr... | [
"TAGS\n#transformers #pytorch #camembert #fr #dataset-piaf #dataset-FQuAD #dataset-SQuAD-FR #arxiv-2004.04906 #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"# dpr-ctx_encoder-fr_qa-camembert",
"## Description\n\nFrench DPR model using CamemBERT as base and then fine-tuned on a combo of three French Q&... |
feature-extraction | transformers |
# dpr-question_encoder-fr_qa-camembert
## Description
French [DPR model](https://arxiv.org/abs/2004.04906) using [CamemBERT](https://arxiv.org/abs/1911.03894) as base and then fine-tuned on a combo of three French Q&A
## Data
### French Q&A
We use a combination of three French Q&A datasets:
1. [PIAFv1.1](https:/... | {"language": "fr", "datasets": ["piaf", "FQuAD", "SQuAD-FR"]} | AgentPublic/dpr-question_encoder-fr_qa-camembert | null | [
"transformers",
"pytorch",
"camembert",
"feature-extraction",
"fr",
"dataset:piaf",
"dataset:FQuAD",
"dataset:SQuAD-FR",
"arxiv:2004.04906",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.04906",
"1911.03894"
] | [
"fr"
] | TAGS
#transformers #pytorch #camembert #feature-extraction #fr #dataset-piaf #dataset-FQuAD #dataset-SQuAD-FR #arxiv-2004.04906 #arxiv-1911.03894 #endpoints_compatible #region-us
|
# dpr-question_encoder-fr_qa-camembert
## Description
French DPR model using CamemBERT as base and then fine-tuned on a combo of three French Q&A
## Data
### French Q&A
We use a combination of three French Q&A datasets:
1. PIAFv1.1
2. FQuADv1.0
3. SQuAD-FR (SQuAD automatically translated to French)
### Training... | [
"# dpr-question_encoder-fr_qa-camembert",
"## Description\n\nFrench DPR model using CamemBERT as base and then fine-tuned on a combo of three French Q&A",
"## Data",
"### French Q&A \nWe use a combination of three French Q&A datasets: \n\n1. PIAFv1.1\n2. FQuADv1.0\n3. SQuAD-FR (SQuAD automatically translated ... | [
"TAGS\n#transformers #pytorch #camembert #feature-extraction #fr #dataset-piaf #dataset-FQuAD #dataset-SQuAD-FR #arxiv-2004.04906 #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"# dpr-question_encoder-fr_qa-camembert",
"## Description\n\nFrench DPR model using CamemBERT as base and then fine-tuned on a... |
text-classification | transformers |
# Guwen CLS
A Classical Chinese Text Classifier.
See also:
<a href="https://github.com/ethan-yt/guwen-models">
<img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_owne... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "text classificatio"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "text-classification... | ethanyt/guwen-cls | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"text classificatio",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roberta #text-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #text classificatio #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Guwen CLS
A Classical Chinese Text Classifier.
See also:
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a> | [
"# Guwen CLS\n\nA Classical Chinese Text Classifier.\n\nSee also: \n\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #text classificatio #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Guwen CLS\n\nA Classical Chinese Text Classifier.\n\nSee also: \n\n<a href=\"U... |
token-classification | transformers |
# Guwen NER
A Classical Chinese Named Entity Recognizer.
Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to
[Guwen Models](https://github.com/ethan-yt/guwen-models).
See also:
<a href="https:/... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classification", "widget": [{"text"... | ethanyt/guwen-ner | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Guwen NER
A Classical Chinese Named Entity Recognizer.
Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to
Guwen Models.
See also:
<a href="URL
<img align="center" width="400" src="URL />
<... | [
"# Guwen NER\n\nA Classical Chinese Named Entity Recognizer.\n\nNote: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to\nGuwen Models.\n\nSee also: \n\n<a href=\"URL\n <img align=\"center\" width=\"... | [
"TAGS\n#transformers #pytorch #jax #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Guwen NER\n\nA Classical Chinese Named Entity Recognizer.\n\nNote: There are some problems w... |
token-classification | transformers |
# Guwen Punc
A Classical Chinese Punctuation Marker.
See also:
<a href="https://github.com/ethan-yt/guwen-models">
<img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "punctuation marker"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classificatio... | ethanyt/guwen-punc | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"punctuation marker",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #punctuation marker #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Guwen Punc
A Classical Chinese Punctuation Marker.
See also:
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a> | [
"# Guwen Punc\n\nA Classical Chinese Punctuation Marker.\n\nSee also: \n\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>"
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #punctuation marker #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Guwen Punc\n\nA Classical Chinese Punctuation Marker.\n\nSee also: \n\n<a hre... |
token-classification | transformers |
# Guwen Quote
A Classical Chinese Quotation Detector.
Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to
[Guwen Models](https://github.com/ethan-yt/guwen-models).
See also:
<a href="https://gi... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "quotation detection"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classificati... | ethanyt/guwen-quote | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"quotation detection",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #quotation detection #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Guwen Quote
A Classical Chinese Quotation Detector.
Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to
Guwen Models.
See also:
<a href="URL
<img align="center" width="400" src="URL />
</a>... | [
"# Guwen Quote\n\nA Classical Chinese Quotation Detector.\n\nNote: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to\nGuwen Models.\n\nSee also: \n\n<a href=\"URL\n <img align=\"center\" width=\"400... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #quotation detection #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Guwen Quote\n\nA Classical Chinese Quotation Detector.\n\nNote: There are so... |
token-classification | transformers |
# Guwen Seg
A Classical Chinese Sentence Segmenter.
See also:
<a href="https://github.com/ethan-yt/guwen-models">
<img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=fff&show_o... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "sentence segmentation"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "token-classifica... | ethanyt/guwen-seg | null | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"sentence segmentation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #sentence segmentation #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Guwen Seg
A Classical Chinese Sentence Segmenter.
See also:
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a> | [
"# Guwen Seg\n\nA Classical Chinese Sentence Segmenter.\n\nSee also: \n\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>"
] | [
"TAGS\n#transformers #pytorch #roberta #token-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #sentence segmentation #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Guwen Seg\n\nA Classical Chinese Sentence Segmenter.\n\nSee also: \n\n<a h... |
text-classification | transformers |
# Guwen Sent
A Classical Chinese Poem Sentiment Classifier.
See also:
<a href="https://github.com/ethan-yt/guwen-models">
<img align="center" width="400" src="https://github-readme-stats.vercel.app/api/pin/?username=ethan-yt&repo=guwen-models&bg_color=30,e96443,904e95&title_color=fff&text_color=fff&icon_color=ff... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch", "sentiment classificatio"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "text-classific... | ethanyt/guwen-sent | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"sentiment classificatio",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #roberta #text-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #sentiment classificatio #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Guwen Sent
A Classical Chinese Poem Sentiment Classifier.
See also:
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a>
<a href="URL
<img align="center" width="400" src="URL />
</a> | [
"# Guwen Sent\n\nA Classical Chinese Poem Sentiment Classifier.\n\nSee also: \n\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>\n<a href=\"URL\n <img align=\"center\" width=\"400\" src=\"URL />\n</a>"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #chinese #classical chinese #literary chinese #ancient chinese #bert #sentiment classificatio #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Guwen Sent\n\nA Classical Chinese Poem Sentiment Classifier.\n\nSee also:... |
fill-mask | transformers |
# GuwenBERT
## Model description
This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recogniti... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widg... | ethanyt/guwenbert-base | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #bert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| GuwenBERT
=========
Model description
-----------------
!GuwenBERT
This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recognition, and so on.
For more information about RoBERTa, take a look at the RoBER... | [
"### \"Gulian Cup\" Ancient Books Named Entity Recognition Evaluation\n\n\nSecond place in the competition. Detailed test results:\n\n\n\nAbout Us\n--------\n\n\nWe are from Datahammer, Beijing Institute of Technology.\nFor more cooperation, please contact email: ethanyt [at] URL\n\n\n\n> \n> Created with ️ by Tan ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #bert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### \"Gulian Cup\" Ancient Books Named Entity Recognition Evaluation\n\n\nSecond place in the competition... |
fill-mask | transformers |
# GuwenBERT
## Model description
This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recogniti... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "pytorch"], "thumbnail": "https://user-images.githubusercontent.com/9592150/97142000-cad08e00-179a-11eb-88df-aff9221482d8.png", "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widg... | ethanyt/guwenbert-large | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"bert",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #bert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| GuwenBERT
=========
Model description
-----------------
!GuwenBERT
This is a RoBERTa model pre-trained on Classical Chinese. You can fine-tune GuwenBERT for downstream tasks, such as sentence breaking, punctuation, named entity recognition, and so on.
For more information about RoBERTa, take a look at the RoBER... | [
"### \"Gulian Cup\" Ancient Books Named Entity Recognition Evaluation\n\n\nSecond place in the competition. Detailed test results:\n\n\n\nAbout Us\n--------\n\n\nWe are from Datahammer, Beijing Institute of Technology.\nFor more cooperation, please contact email: ethanyt [at] URL\n\n\n\n> \n> Created with ️ by Tan ... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #bert #zh #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### \"Gulian Cup\" Ancient Books Named Entity Recognition Evaluation\n\n\nSecond place in the competition... |
text-generation | transformers | # ai-msgbot GPT2-L + daily dialogues
_NOTE: this model card is a WIP_
GPT2-L (774M parameters) fine-tuned on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using `aitextgen`. This model was then subsequently further fine-tuned on the [Daily Dialogues](http://yanran.li/dailydialog) dataset for ... | {} | ethzanalytics/ai-msgbot-gpt2-L-dialogue | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ai-msgbot GPT2-L + daily dialogues
_NOTE: this model card is a WIP_
GPT2-L (774M parameters) fine-tuned on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using 'aitextgen'. This model was then subsequently further fine-tuned on the Daily Dialogues dataset for an additional 40k steps, this ti... | [
"# ai-msgbot GPT2-L + daily dialogues\n\n_NOTE: this model card is a WIP_\n\nGPT2-L (774M parameters) fine-tuned on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using 'aitextgen'. This model was then subsequently further fine-tuned on the Daily Dialogues dataset for an additional 40k steps... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ai-msgbot GPT2-L + daily dialogues\n\n_NOTE: this model card is a WIP_\n\nGPT2-L (774M parameters) fine-tuned on the Wizard of Wikipedia dataset for 40k steps... |
text-generation | transformers | # ai-msgbot GPT2-L
_NOTE: model card is WIP_
GPT2-L (774M parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with 34/36 layers frozen using `aitextgen`.
Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an open-ende... | {} | ethzanalytics/ai-msgbot-gpt2-L | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ai-msgbot GPT2-L
_NOTE: model card is WIP_
GPT2-L (774M parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using 'aitextgen'.
Designed for use with ai-msgbot to create an open-ended chatbot (of course, if other use cases arise have at it).
## conversation data
The da... | [
"# ai-msgbot GPT2-L\n\n_NOTE: model card is WIP_\n\nGPT2-L (774M parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using 'aitextgen'. \n\n\nDesigned for use with ai-msgbot to create an open-ended chatbot (of course, if other use cases arise have at it).",
"## conversati... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ai-msgbot GPT2-L\n\n_NOTE: model card is WIP_\n\nGPT2-L (774M parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 34/36 layers frozen using 'aitextge... |
text-generation | transformers | # ai-msgbot GPT-2 M Conversational
A GPT-2 M 355M parameter model for usage with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create a chatbot-like tool.
This model was fine-tuned on a parsed version of [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 10,000 steps. 20/24 l... | {} | ethzanalytics/ai-msgbot-gpt2-M | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # ai-msgbot GPT-2 M Conversational
A GPT-2 M 355M parameter model for usage with ai-msgbot to create a chatbot-like tool.
This model was fine-tuned on a parsed version of the Wizard of Wikipedia dataset for 10,000 steps. 20/24 layers were frozen for the fine-tuning process.
## conversation data
The dataset was tok... | [
"# ai-msgbot GPT-2 M Conversational\n\nA GPT-2 M 355M parameter model for usage with ai-msgbot to create a chatbot-like tool.\n\nThis model was fine-tuned on a parsed version of the Wizard of Wikipedia dataset for 10,000 steps. 20/24 layers were frozen for the fine-tuning process.",
"## conversation data\n\nThe d... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# ai-msgbot GPT-2 M Conversational\n\nA GPT-2 M 355M parameter model for usage with ai-msgbot to create a chatbot-like tool.\n\nThis model was fine-tuned on a par... |
text-generation | transformers |
# ai-msgbot: GPT2-XL-dialogue
GPT2-XL (~1.5 B parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with **33**/36 layers frozen using `aitextgen`. The resulting model was then **further fine-tuned** on the [Daily Dialogues](http://yanran.li/dailydialog... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt"], "datasets": ["natural_questions"], "widget": [{"text": "Do you like my new haircut?\nperson beta:\n\n", "example_title": "haircut"}, {"text": "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n", "examp... | ethzanalytics/ai-msgbot-gpt2-XL-dialogue | null | [
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"safetensors",
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"en",
"dataset:natural_questions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #gpt #en #dataset-natural_questions #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# ai-msgbot: GPT2-XL-dialogue
GPT2-XL (~1.5 B parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 33/36 layers frozen using 'aitextgen'. The resulting model was then further fine-tuned on the Daily Dialogues for 40k steps, with 34/36 layers frozen.
Designed for use with ai-msgbot to create an... | [
"# ai-msgbot: GPT2-XL-dialogue\n\n\nGPT2-XL (~1.5 B parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 33/36 layers frozen using 'aitextgen'. The resulting model was then further fine-tuned on the Daily Dialogues for 40k steps, with 34/36 layers frozen.\n\n\nDesigned for use with ai-msgbot to... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #gpt #en #dataset-natural_questions #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# ai-msgbot: GPT2-XL-dialogue\n\n\nGPT2-XL (~1.5 B parameters) trained on the Wizard of Wikipedia da... |
text-generation | transformers | # ai-msgbot GPT2-XL
_NOTE: model card is WIP_
GPT2-XL (~1.5 B parameters) trained on [the Wizard of Wikipedia dataset](https://parl.ai/projects/wizard_of_wikipedia/) for 40k steps with **33**/36 layers frozen using `aitextgen`.
Designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot) to create an o... | {"language": ["en"], "license": "mit", "tags": ["text-generation", "gpt2", "gpt"], "datasets": ["natural questions"], "widget": [{"text": "Do you like my new haircut?\nperson beta:\n\n", "example_title": "haircut"}, {"text": "I love to learn new things.. are you willing to teach me something?\nperson beta:\n\n", "examp... | ethzanalytics/ai-msgbot-gpt2-XL | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #gpt #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # ai-msgbot GPT2-XL
_NOTE: model card is WIP_
GPT2-XL (~1.5 B parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 33/36 layers frozen using 'aitextgen'.
Designed for use with ai-msgbot to create an open-ended chatbot (of course, if other use cases arise, have at it).
## conversation data
T... | [
"# ai-msgbot GPT2-XL\n\n_NOTE: model card is WIP_\n\nGPT2-XL (~1.5 B parameters) trained on the Wizard of Wikipedia dataset for 40k steps with 33/36 layers frozen using 'aitextgen'. \n\n\nDesigned for use with ai-msgbot to create an open-ended chatbot (of course, if other use cases arise, have at it).",
"## conve... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# ai-msgbot GPT2-XL\n\n_NOTE: model card is WIP_\n\nGPT2-XL (~1.5 B parameters) trained on the Wizard of Wikipedia dataset for 40k steps wit... |
text-generation | transformers |
# distilgpt2-tiny-conversational
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a parsed version of Wizard of Wikipedia. Persona alpha/beta framework designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot).
It achieves the following results on the evaluation... | {"license": "apache-2.0", "tags": ["text-generation", "chatbot", "dialogue", "distilgpt2", "gpt2", "ai-msgbot"], "widget": [{"text": "I know you're tired, but can we go for another walk this evening?\nperson beta:\n\n", "example_title": "walk"}, {"text": "Have you done anything exciting lately?\nperson beta:\n\n", "exa... | ethzanalytics/distilgpt2-tiny-conversational | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"chatbot",
"dialogue",
"distilgpt2",
"ai-msgbot",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #chatbot #dialogue #distilgpt2 #ai-msgbot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| distilgpt2-tiny-conversational
==============================
This model is a fine-tuned version of distilgpt2 on a parsed version of Wizard of Wikipedia. Persona alpha/beta framework designed for use with ai-msgbot.
It achieves the following results on the evaluation set:
* Loss: 2.2461
Model description
-------... | [
"### 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* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam ... | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #chatbot #dialogue #distilgpt2 #ai-msgbot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during traini... |
text-generation | transformers | #blabla | {"tags": ["conversational"]} | ethzhou/newJooby | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| #blabla | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | transformers | # Attention in Attention Network for Image Super-Resolution (A2N)
A2N model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Attention in Attention Network for Image Super-Resolution](https://arxiv.org/a... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/a2n | null | [
"transformers",
"A2N",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2104.09497",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",... | null | 2022-03-02T23:29:05+00:00 | [
"2104.09497",
"2104.07566"
] | [] | TAGS
#transformers #A2N #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.09497 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Attention in Attention Network for Image Super-Resolution (A2N)
===============================================================
A2N model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Attention in At... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #A2N #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.09497 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to u... |
null | null |
# AniCharaGAN: Anime Character Generation with StyleGAN2
[](https://github.com/eugenesiow/practical-ml)
This model uses the awesome lucidrains’s [stylegan2-pytorch](https://github.com/lucidrains/stylegan2-pytorch) library t... | {"license": "apache-2.0", "tags": ["stylegan2", "image-generation"]} | eugenesiow/ani-chara-gan | null | [
"stylegan2",
"image-generation",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stylegan2 #image-generation #license-apache-2.0 #region-us
|
# AniCharaGAN: Anime Character Generation with StyleGAN2
![GitHub Repo stars](URL
This model uses the awesome lucidrains’s stylegan2-pytorch library to train a model on a private anime character dataset to generate full-body 256x256 female anime characters.
Here are some samples:
!Samples of anime characters and ... | [
"# AniCharaGAN: Anime Character Generation with StyleGAN2\n\n![GitHub Repo stars](URL\n\nThis model uses the awesome lucidrains’s stylegan2-pytorch library to train a model on a private anime character dataset to generate full-body 256x256 female anime characters.\n\nHere are some samples:\n\n!Samples of anime cha... | [
"TAGS\n#stylegan2 #image-generation #license-apache-2.0 #region-us \n",
"# AniCharaGAN: Anime Character Generation with StyleGAN2\n\n
AWSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Lightweight Image Super-Resolution with Adaptive Weighted... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/awsrn-bam | null | [
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"arxiv:1904.02358",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [
"1904.02358",
"2104.07566"
] | [] | TAGS
#transformers #AWSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1904.02358 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Lightweight Image Super-Resolution with Adaptive Weighted Learning Network (AWSRN)
==================================================================================
AWSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #AWSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1904.02358 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to... |
text2text-generation | transformers | # BART Paraphrase Model (Large)
A large BART seq2seq (text2text generation) model fine-tuned on 3 paraphrase datasets.
## Model description
The BART model was proposed in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461... | {"language": "en", "license": "apache-2.0", "tags": ["transformers", "bart", "paraphrase", "seq2seq"], "datasets": ["quora", "paws"]} | eugenesiow/bart-paraphrase | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"paraphrase",
"seq2seq",
"en",
"dataset:quora",
"dataset:paws",
"arxiv:1910.13461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.13461"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #paraphrase #seq2seq #en #dataset-quora #dataset-paws #arxiv-1910.13461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BART Paraphrase Model (Large)
A large BART seq2seq (text2text generation) model fine-tuned on 3 paraphrase datasets.
## Model description
The BART model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. (2019).
- Bart us... | [
"# BART Paraphrase Model (Large)\nA large BART seq2seq (text2text generation) model fine-tuned on 3 paraphrase datasets.",
"## Model description\nThe BART model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. (2019).... | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #paraphrase #seq2seq #en #dataset-quora #dataset-paws #arxiv-1910.13461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BART Paraphrase Model (Large)\nA large BART seq2seq (text2text generation) mo... |
null | transformers | # Cascading Residual Network (CARN)
CARN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network](https://arxiv.org/abs/180... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/carn-bam | null | [
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"dataset:eugenesiow/Urban100",
"arxiv:1803.08664",
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"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1803.08664",
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] | [] | TAGS
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| Cascading Residual Network (CARN)
=================================
CARN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual N... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #CARN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1803.08664 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to ... |
null | transformers | # Cascading Residual Network (CARN)
CARN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network](https://arxiv.org/abs/180... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/carn | null | [
"transformers",
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"super-image",
"image-super-resolution",
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"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1803.08664",
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] | [] | TAGS
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| Cascading Residual Network (CARN)
=================================
CARN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual N... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #CARN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1803.08664 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to ... |
null | transformers | # Densely Residual Laplacian Super-Resolution (DRLN)
DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/drln-bam | null | [
"transformers",
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"arxiv:1906.12021",
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"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1906.12021",
"2104.07566"
] | [] | TAGS
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| Densely Residual Laplacian Super-Resolution (DRLN)
==================================================
DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Densely Residual Laplacian Super-resolut... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #DRLN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1906.12021 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nTh... |
null | transformers | # Densely Residual Laplacian Super-Resolution (DRLN)
DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/drln | null | [
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"arxiv:1906.12021",
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"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1906.12021",
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] | [] | TAGS
#transformers #DRLN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1906.12021 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Densely Residual Laplacian Super-Resolution (DRLN)
==================================================
DRLN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Densely Residual Laplacian Super-resolut... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #DRLN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1906.12021 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to ... |
null | transformers | # Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](h... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/edsr-base | null | [
"transformers",
"EDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1707.02921",
"2104.07566"
] | [] | TAGS
#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
========================================================================
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the pa... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to ... |
null | transformers | # Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](h... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/edsr | null | [
"transformers",
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"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1707.02921",
"2104.07566"
] | [] | TAGS
#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
========================================================================
EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the pa... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #EDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to ... |
null | transformers | # Holistic Attention Network (HAN)
HAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Single Image Super-Resolution via a Holistic Attention Network](https://arxiv.org/abs/2008.08767) by Niu et a... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/han | null | [
"transformers",
"HAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2008.08767",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",... | null | 2022-03-02T23:29:05+00:00 | [
"2008.08767",
"2104.07566"
] | [] | TAGS
#transformers #HAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2008.08767 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Holistic Attention Network (HAN)
================================
HAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Single Image Super-Resolution via a Holistic Attention Network by Niu et al. ... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #HAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2008.08767 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to u... |
null | transformers | # Multi-Scale Deep Super-Resolution System (MDSR)
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/mdsr-bam | null | [
"transformers",
"MDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1707.02921",
"2104.07566"
] | [] | TAGS
#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
| Multi-Scale Deep Super-Resolution System (MDSR)
===============================================
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Ima... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nTh... |
null | transformers | # Multi-Scale Deep Super-Resolution System (MDSR)
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/mdsr | null | [
"transformers",
"MDSR",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1707.02921",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1707.02921",
"2104.07566"
] | [] | TAGS
#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Multi-Scale Deep Super-Resolution System (MDSR)
===============================================
MDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Enhanced Deep Residual Networks for Single Ima... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #MDSR #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1707.02921 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to ... |
null | transformers | # Multi-scale Residual Network for Image Super-Resolution (MSRN)
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.th... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/msrn-bam | null | [
"transformers",
"MSRN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.07566"
] | [] | TAGS
#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
| Multi-scale Residual Network for Image Super-Resolution (MSRN)
==============================================================
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Multi-scale Resi... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe model can be use... |
null | transformers | # Multi-scale Residual Network for Image Super-Resolution (MSRN)
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Multi-scale Residual Network for Image Super-Resolution](https://openaccess.th... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/msrn | null | [
"transformers",
"MSRN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"re... | null | 2022-03-02T23:29:05+00:00 | [
"2104.07566"
] | [] | TAGS
#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Multi-scale Residual Network for Image Super-Resolution (MSRN)
==============================================================
MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Multi-scale Resi... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #MSRN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nThe model... |
null | transformers | # Pixel Attention Network (PAN)
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) ... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/pan-bam | null | [
"transformers",
"PAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
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"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2010.01073",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",... | null | 2022-03-02T23:29:05+00:00 | [
"2010.01073",
"2104.07566"
] | [] | TAGS
#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
| Pixel Attention Network (PAN)
=============================
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Efficient Image Super-Resolution Using Pixel Attention by Zhao et al. (2020) and fi... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nThe... |
null | transformers | # Pixel Attention Network (PAN)
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) ... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/pan | null | [
"transformers",
"PAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2010.01073",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",... | null | 2022-03-02T23:29:05+00:00 | [
"2010.01073",
"2104.07566"
] | [] | TAGS
#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Pixel Attention Network (PAN)
=============================
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Efficient Image Super-Resolution Using Pixel Attention by Zhao et al. (2020) and fi... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #PAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-2010.01073 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### How to u... |
null | transformers | # Residual Channel Attention Networks (RCAN)
RCAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://arxiv.org/abs... | {"license": "apache-2.0", "tags": ["super-image", "image-super-resolution"], "datasets": ["eugenesiow/Div2k", "eugenesiow/Set5", "eugenesiow/Set14", "eugenesiow/BSD100", "eugenesiow/Urban100"], "metrics": ["pnsr", "ssim"]} | eugenesiow/rcan-bam | null | [
"transformers",
"RCAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:1807.02758",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible"... | null | 2022-03-02T23:29:05+00:00 | [
"1807.02758",
"2104.07566"
] | [] | TAGS
#transformers #RCAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1807.02758 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us
| Residual Channel Attention Networks (RCAN)
==========================================
RCAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Image Super-Resolution Using Very Deep Residual Channel ... | [
"### How to use\n\n\nThe model can be used with the super\\_image library:\n\n\nHere is how to use a pre-trained model to upscale your image:\n\n\n![Open In Colab](URL \"Open in Colab\")\n\n\nTraining data\n-------------\n\n\nThe models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of... | [
"TAGS\n#transformers #RCAN #super-image #image-super-resolution #dataset-eugenesiow/Div2k #dataset-eugenesiow/Set5 #dataset-eugenesiow/Set14 #dataset-eugenesiow/BSD100 #dataset-eugenesiow/Urban100 #arxiv-1807.02758 #arxiv-2104.07566 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### How to use\n\n\nTh... |
feature-extraction | transformers | korean Mental Health BERT
kcBERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
@inproceedings{le... | {} | eunjin/koMHBERT-kcbert-based-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| korean Mental Health BERT
kcBERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 2만9천개)
@inproceedings{le... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | korean Mental Health BERT -v2
huggingface에 공개된 kcbert-base BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다. 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: http://www.psychiatricnews.net | {} | eunjin/koMHBERT-kcbert-based-v2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| korean Mental Health BERT -v2
huggingface에 공개된 kcbert-base BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다. 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: URL | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | korean Mental Health BERT
huggingface에 공개된 KR-Medium BERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다. 이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 ... | {} | eunjin/koMHBERT-krbert-based-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| korean Mental Health BERT
huggingface에 공개된 KR-Medium BERT를 아래의 dataset으로 MLM fine-tuining한 Bert Model입니다. 정신건강 문제 해결에 도움이 될만한 데이터셋이라고 판단하여 domain-adaptation하였고, 향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다. 이후 공개될 예정인 더 큰 규모의 데이터셋까지 Dapt할 예정입니다.
datasets from AIhub
웰니스 대화 스크립트 데이터셋1 & 2 (중복 제거 약 ... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | korean Mental Health BERT -v2
huggingface에 공개된 KR-Medium BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다.
정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다.
향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: http://www.psychiatricnews.net | {} | eunjin/koMHBERT-krbert-based-v2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| korean Mental Health BERT -v2
huggingface에 공개된 KR-Medium BERT를 정신건강의학신문을 크롤링한 dataset으로 MLM fine-tuining한 Bert Model입니다.
정신건강 발화 관련 데이터를 모을 수 없는 상황에서 이를 대체할만한 데이터로 제시합니다.
향후 정신건강 관련 감정 및 상태 classification 및 그에 따른 chatbot 구현에 사용할 수 있습니다.
정신건강의학신문: URL | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
text-generation | transformers | * skt/kogpt2-base-v2에 wellness 및 일상챗봇 데이터를 fine-tuning한 모델입니다.
* 유사한 정신건강 상담 도메인에서 바로 사용 가능합니다.
* 깃허브 사이트를 참조해주세요! https://github.com/eunjiinkim/WellnessChatbot | {} | eunjin/kogpt2-finetuned-wellness | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| * skt/kogpt2-base-v2에 wellness 및 일상챗봇 데이터를 fine-tuning한 모델입니다.
* 유사한 정신건강 상담 도메인에서 바로 사용 가능합니다.
* 깃허브 사이트를 참조해주세요! URL | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310788
- CO2 Emissions (in grams): 6.826886567147602
## Validation Metrics
- Loss: 0.20949310064315796
- Accuracy: 0.9578392621870883
- Precision: 0.9476190476190476
- Recall: 0.9045454545454545
- AUC: 0.9714032720526227
- F1: 0.9255... | {"language": "unk", "tags": "autonlp", "datasets": ["evandrodiniz/autonlp-data-api-boamente"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 6.826886567147602} | evandrodiniz/autonlp-api-boamente-417310788 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:evandrodiniz/autonlp-data-api-boamente",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310788
- CO2 Emissions (in grams): 6.826886567147602
## Validation Metrics
- Loss: 0.20949310064315796
- Accuracy: 0.9578392621870883
- Precision: 0.9476190476190476
- Recall: 0.9045454545454545
- AUC: 0.9714032720526227
- F1: 0.9255... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310788\n- CO2 Emissions (in g... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310793
- CO2 Emissions (in grams): 9.446754273734577
## Validation Metrics
- Loss: 0.25755178928375244
- Accuracy: 0.9407114624505929
- Precision: 0.8600823045267489
- Recall: 0.95
- AUC: 0.9732501264968797
- F1: 0.9028077753779697
... | {"language": "unk", "tags": "autonlp", "datasets": ["evandrodiniz/autonlp-data-api-boamente"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 9.446754273734577} | evandrodiniz/autonlp-api-boamente-417310793 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:evandrodiniz/autonlp-data-api-boamente",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-evandrodiniz/autonlp-data-api-boamente #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 417310793
- CO2 Emissions (in grams): 9.446754273734577
## Validation Metrics
- Loss: 0.25755178928375244
- Accuracy: 0.9407114624505929
- Precision: 0.8600823045267489
- Recall: 0.95
- AUC: 0.9732501264968797
- F1: 0.9028077753779697
... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 417310793\n- CO2 Emissions (in g... |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Afrikaans-AfriBooms
| Feature | Description |
| --- | --- |
| **Name** | `af_udv25_afrikaansafribooms_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `e... | {"language": ["af"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/af_udv25_afrikaansafribooms_trf | null | [
"spacy",
"token-classification",
"af",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"af"
] | TAGS
#spacy #token-classification #af #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Afrikaans-AfriBooms
### Label Scheme
View label scheme (455 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (455 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #af #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (455 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Danish-DDT
| Feature | Description |
| --- | --- |
| **Name** | `da_udv25_danishddt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | {"language": ["da"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/da_udv25_danishddt_trf | null | [
"spacy",
"token-classification",
"da",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Danish-DDT
### Label Scheme
View label scheme (1316 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1316 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #da #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1316 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_German-HDT
| Feature | Description |
| --- | --- |
| **Name** | `de_udv25_germanhdt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_t... | {"language": ["de"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/de_udv25_germanhdt_trf | null | [
"spacy",
"token-classification",
"de",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#spacy #token-classification #de #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_German-HDT
### Label Scheme
View label scheme (62832 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (62832 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #de #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (62832 labels for 6 components)",
"### Accuracy"
] |
text-classification | spacy |
# Welcome to Healthsea ✨
Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classification model with C... | {"language": ["en"], "tags": ["spacy", "token-classification", "text-classification"]} | explosion/en_healthsea | null | [
"spacy",
"token-classification",
"text-classification",
"en",
"model-index",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #text-classification #en #model-index #has_space #region-us
| Welcome to Healthsea
====================
Create better access to health with machine learning and natural language processing. This is the trained healthsea pipeline for analyzing user reviews to supplements by extracting their effects on health. This pipeline features a trained NER model and a custom Text Classific... | [
"### Label Scheme\n\n\n\nView label scheme (6 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #text-classification #en #model-index #has_space #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (6 labels for 2 components)",
"### Accuracy"
] |
text-classification | spacy | # 🪐 spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the [GoEmotions dataset](https://github.com/google-research/google-research/tree/master/goemotions)
| Feature | Description |
| --- | --- |
| **Name** | `en_textcat_goemotions` |
|... | {"language": ["en"], "license": "mit", "tags": ["spacy", "text-classification"], "model-index": [{"name": "en_textcat_goemotions", "results": []}]} | explosion/en_textcat_goemotions | null | [
"spacy",
"text-classification",
"en",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #text-classification #en #license-mit #region-us
| spaCy Project: Categorization of emotions in Reddit posts (Text Classification) This project uses spaCy to train a text classifier on the GoEmotions dataset
============================================================================================================================================================
> ... | [
"### Label Scheme\n\n\n\nView label scheme (28 labels for 1 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #text-classification #en #license-mit #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (28 labels for 1 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_English-EWT
| Feature | Description |
| --- | --- |
| **Name** | `en_udv25_englishewt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit... | {"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/en_udv25_englishewt_trf | null | [
"spacy",
"token-classification",
"en",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_English-EWT
### Label Scheme
View label scheme (1760 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1760 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1760 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Spanish-AnCora
| Feature | Description |
| --- | --- |
| **Name** | `es_udv25_spanishancora_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimenta... | {"language": ["es"], "license": "gpl-3.0", "tags": ["spacy", "token-classification"]} | explosion/es_udv25_spanishancora_trf | null | [
"spacy",
"token-classification",
"es",
"license:gpl-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Spanish-AnCora
### Label Scheme
View label scheme (2060 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (2060 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (2060 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Finnish-TDT
| Feature | Description |
| --- | --- |
| **Name** | `fi_udv25_finnishtdt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit... | {"language": ["fi"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/fi_udv25_finnishtdt_trf | null | [
"spacy",
"token-classification",
"fi",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fi"
] | TAGS
#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Finnish-TDT
### Label Scheme
View label scheme (12912 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (12912 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fi #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (12912 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_French-Sequoia
| Feature | Description |
| --- | --- |
| **Name** | `fr_udv25_frenchsequoia_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimenta... | {"language": ["fr"], "license": "lgpl-lr", "tags": ["spacy", "token-classification"]} | explosion/fr_udv25_frenchsequoia_trf | null | [
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_French-Sequoia
### Label Scheme
View label scheme (916 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (916 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #fr #license-lgpl-lr #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (916 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Irish-IDT
| Feature | Description |
| --- | --- |
| **Name** | `ga_udv25_irishidt_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tre... | {"language": ["ga"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/ga_udv25_irishidt_trf | null | [
"spacy",
"token-classification",
"ga",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ga"
] | TAGS
#spacy #token-classification #ga #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Irish-IDT
### Label Scheme
View label scheme (1662 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (1662 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #ga #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (1662 labels for 6 components)",
"### Accuracy"
] |
token-classification | spacy | UD v2.5 benchmarking pipeline for UD_Croatian-SET
| Feature | Description |
| --- | --- |
| **Name** | `hr_udv25_croatianset_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | {"language": ["hr"], "license": "cc-by-sa-4.0", "tags": ["spacy", "token-classification"]} | explosion/hr_udv25_croatianset_trf | null | [
"spacy",
"token-classification",
"hr",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hr"
] | TAGS
#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us
| UD v2.5 benchmarking pipeline for UD\_Croatian-SET
### Label Scheme
View label scheme (3855 labels for 6 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (3855 labels for 6 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #hr #license-cc-by-sa-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3855 labels for 6 components)",
"### Accuracy"
] |
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