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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 18:52:31
| downloads
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| likes
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11.7k
| library_name
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jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-2_austria-8_s458
|
jonatasgrosman
| 2022-07-25T13:40:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T13:40:42Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_xls-r_accent_germany-2_austria-8_s458
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-2_austria-8_s368
|
jonatasgrosman
| 2022-07-25T13:36:22Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T13:35:55Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_xls-r_accent_germany-2_austria-8_s368
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s728
|
jonatasgrosman
| 2022-07-25T13:26:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T13:26:38Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_xls-r_accent_germany-10_austria-0_s728
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
benjamyu/autotrain-ms-2-1174443640
|
benjamyu
| 2022-07-25T13:26:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain",
"en",
"dataset:benjamyu/autotrain-data-ms-2",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-25T03:54:44Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- benjamyu/autotrain-data-ms-2
co2_eq_emissions: 4.619328856849087
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1174443640
- CO2 Emissions (in grams): 4.619328856849087
## Validation Metrics
- Loss: 2.689530849456787
- Rouge1: 15.9713
- Rouge2: 2.1067
- RougeL: 12.1778
- RougeLsum: 13.5772
- Gen Len: 18.9798
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/benjamyu/autotrain-ms-2-1174443640
```
|
mtreviso/ct5-small-en-wiki-l2r
|
mtreviso
| 2022-07-25T13:22:55Z | 22 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"en",
"dataset:wikipedia",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-17T04:48:26Z |
---
license: afl-3.0
language: en
tags:
- t5
datasets:
- wikipedia
---
# cT5-small left-to-right
Github: https://github.com/mtreviso/chunked-t5
This is a variant of [cT5](https://huggingface.co/mtreviso/ct5-small-en-wiki) that was trained with a left-to-right autoregressive decoding mask. As a consequence, it does not support parallel decoding, but it still predicts the end-of-chunk token `</c>` at the end of each chunk.
|
mtreviso/ct5-small-en-wiki
|
mtreviso
| 2022-07-25T13:19:21Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"en",
"dataset:wikipedia",
"license:afl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-21T06:28:42Z |
---
license: afl-3.0
language: en
tags:
- t5
datasets:
- wikipedia
---
# chunked T5 - small (cT5-small)
Github: https://github.com/mtreviso/chunked-t5
A T5 model that uses a new loss where a special end-of-chunk token `</c>` is appended after sentinel tokens.
The decoder has to predict the full input with masked tokens followed by `</c>`.
This allows a much faster auto-regressive generation since the decoder can predict multiple tokens in parallel.
For example, for the input `the quick brown fox jumps over the lazy dog`:
```
encoder: the <extra_id_0> fox jumps <extra_id_1> the lazy dog
T5 decoder : <extra_id_0> quick brown <extra_id_1> over <extra_id_2>
cT5 decoder: <extra_id_0> quick brown </c> <extra_id_1> over </c> <extra_id_2>
```
The generation may look like this for T5 and cT5:
```
T5: <extra_id_0>
T5: <extra_id_0> quick
T5: <extra_id_0> quick brown
T5: <extra_id_0> quick brown <extra_id_1>
T5: <extra_id_0> quick brown <extra_id_1> over
T5: <extra_id_0> quick brown <extra_id_1> over <extra_id_2>
T5: <extra_id_0> quick brown <extra_id_1> over <extra_id_2> </s>
cT5: <extra_id_0> <pad> <extra_id_1> <pad> <extra_id_2> </s>
cT5: <extra_id_0> quick <pad> <extra_id_1> over <pad> <extra_id_2> </s>
cT5: <extra_id_0> quick brown <pad> <extra_id_1> over </c> <extra_id_2> </s>
cT5: <extra_id_0> quick brown </c> <extra_id_1> over </c> <extra_id_2> </s>
```
In the original T5, the decoder is called \\(n_s + 1 + \sum_i |s_i|\\) times autoregressively,
where \\(n_s\\) is the number of sentinel tokens and \\(s_1,...,s_{n_s}\\) are the predicted chunks.
In contrast, cT5's decoder is called just \\(max_i |s_i| + 1\\) times.
The generation stops when all sentences were fully translated to complete chunks, i.e., until all `</c>` tokens were generated.
Alternatively, you can also set `max_chunk_size` to manually force the model to stop after generating a chunk with `max_chunk_size` tokens.
The overhead of calling the decoder with a longer input is less pronounced since this computation can be parallelized in GPUs/TPUs.
## Training details
cT5 models used T5's weights as a starting point, and then it was finetuned on the
English [wikipedia](https://huggingface.co/datasets/wikipedia) for 3 epochs,
achieving ~74% validation accuracy (ct5-small).
The training script is in JAX + Flax and can be found in `pretrain_ct5.py`.
Flax checkpoints can be converted to PyTorch via `convert_flax_to_pytorch.py [flax_dirname]`.
## Checkpoints
- ct5-small: https://huggingface.co/mtreviso/ct5-small-en-wiki
- ct5-base: todo
- ct5-large: todo
## Usage
```python
from transformers import AutoTokenizer
from modeling_ct5 import CT5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("mtreviso/ct5-small-en-wiki")
model = CT5ForConditionalGeneration.from_pretrained("mtreviso/ct5-small-en-wiki")
```
For training:
```python
input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
labels = tokenizer("<extra_id_0> man </c> <extra_id_1> the </c> <extra_id_2>", return_tensors="pt").input_ids
outputs = model(input_ids=input_ids, labels=labels)
loss = outputs.loss
logits = outputs.logits
```
For generation:
```python
texts = [
"The <extra_id_0> walks in <extra_id_1> park",
"UN Chief says there is no way to <extra_id_0> in Syria",
]
input_ids = tokenizer(texts, return_tensors="pt", padding=True).input_ids
generated_ids = model.generate(
input_ids,
use_cache=False, # important to set to False to avoid caching
eoc_token_id=tokenizer.vocab['</c>'], # important to set to the correct end-of-chunk id
max_chunk_size=5, # the default is 9999999, which is a large number
)
```
This will produce the following tokens:
```python
>> ['<pad>', '<extra_id_0>', '▁Walking', '▁Trail', '</c>', '<extra_id_1>', '▁the', '</c>', '<extra_id_2>', '</s>']
>> ['<pad>', '<extra_id_0>', '▁treat', '▁Syria', '</c>', '<extra_id_1>', '</s>', '<pad>', '<pad>', '<pad>']
```
You have to pass `use_cache=False` to `generate()` in order to avoid caching during the generation procedure as caching is not available for parallel decoding.
Currently, parallel decoding is only supported for PyTorch (greedy search, greedy sampling, beam search, beam sampling) and JAX (greedy search and greedy sampling).
**Note on the beam search implementation**: my beam search implementation is slower than optimal.
This is because I use the structures provided by HuggingFace's implementation, namely, BeamScores and BeamHypotheses to store the beam search results for each chunk in the input.
In other words, my implementation computes independent "beams" for each chunk rather than for each input sequence.
It is possible to make it faster by using a custom BeamScores and BeamHypotheses class, but I haven't done that yet.
## Evaluation
See the notebook `evaluate_ct5.ipynb` for an example of how to evaluate cT5 in terms of accuracy and perplexity.
The notebook `profile.ipynb` shows how to profile the model to get runtimes.
Here is a comparison between cT5-small and T5-small on a subset of the WikiText-103 dataset using deterministic greedy search:
| Model | Exact match ↑ | Edit distance ratio ↑ | Perplexity ↓ | Time (seconds) ↓ |
|-------|---------------|----------------------|--------------|-----------------|
| T5-small | 0.11 | 0.60 | 2.22 | 44.71 |
| cT5-small | 0.09 | 0.58 | 1.48 | 10.63 |
On this toy dataset, cT5-small has a lower perplexity while being faster than T5-small. However, more experiments are needed for a rigorous evaluation.
If you are interested in applying cT5 to real data, please contact me.
|
jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-0_austria-10_s350
|
jonatasgrosman
| 2022-07-25T13:06:58Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T13:06:44Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_xls-r_accent_germany-0_austria-10_s350
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_xls-r_accent_germany-5_austria-5_s412
|
jonatasgrosman
| 2022-07-25T12:52:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T12:52:09Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_xls-r_accent_germany-5_austria-5_s412
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-8_female-2_s428
|
jonatasgrosman
| 2022-07-25T12:38:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T12:37:55Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-8_female-2_s428
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-2_female-8_s473
|
jonatasgrosman
| 2022-07-25T12:33:22Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T12:33:11Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-2_female-8_s473
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-2_female-8_s3
|
jonatasgrosman
| 2022-07-25T11:58:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T11:58:21Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-2_female-8_s3
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-2_female-8_s255
|
jonatasgrosman
| 2022-07-25T11:54:01Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T11:53:50Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-2_female-8_s255
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s934
|
jonatasgrosman
| 2022-07-25T11:31:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T11:31:13Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s934
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s469
|
jonatasgrosman
| 2022-07-25T11:26:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T11:26:32Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s469
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
huggingtweets/bwahwtfbwah
|
huggingtweets
| 2022-07-25T11:22:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-25T11:21:28Z |
---
language: en
thumbnail: http://www.huggingtweets.com/bwahwtfbwah/1658748163123/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1543387213370638338/Xn8bL7wJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🤍🖤</div>
<div style="text-align: center; font-size: 14px;">@bwahwtfbwah</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 🤍🖤.
| Data | 🤍🖤 |
| --- | --- |
| Tweets downloaded | 3245 |
| Retweets | 501 |
| Short tweets | 655 |
| Tweets kept | 2089 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/p4n65kie/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bwahwtfbwah's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3pyxv8zk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3pyxv8zk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/bwahwtfbwah')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s400
|
jonatasgrosman
| 2022-07-25T11:22:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T11:21:49Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s400
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-8_belgium-2_s496
|
jonatasgrosman
| 2022-07-25T10:58:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:57:52Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-8_belgium-2_s496
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-2_belgium-8_s709
|
jonatasgrosman
| 2022-07-25T10:48:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:48:42Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-2_belgium-8_s709
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-2_belgium-8_s284
|
jonatasgrosman
| 2022-07-25T10:44:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:43:55Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-2_belgium-8_s284
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s869
|
jonatasgrosman
| 2022-07-25T10:34:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:34:13Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s869
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s271
|
jonatasgrosman
| 2022-07-25T10:24:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:24:41Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-10_belgium-0_s271
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-0_belgium-10_s947
|
jonatasgrosman
| 2022-07-25T10:20:18Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:19:46Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-0_belgium-10_s947
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-0_belgium-10_s693
|
jonatasgrosman
| 2022-07-25T10:13:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:12:51Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-0_belgium-10_s693
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_fr_vp-100k_accent_france-5_belgium-5_s607
|
jonatasgrosman
| 2022-07-25T10:03:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T10:03:13Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- fr
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_fr_vp-100k_accent_france-5_belgium-5_s607
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
MarcoYuTono/huawei-noahTinyBERT_General_6L_768_HotpotQA
|
MarcoYuTono
| 2022-07-25T09:51:16Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"tag1",
"tag2",
"en",
"dataset:HotpotQA",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-22T21:29:10Z |
---
language:
- en
tags:
- tag1
- tag2
license: apache-2.0
datasets:
- HotpotQA
metrics:
- SQuad
---
This model fine-tuned `huawei-noahTinyBERT_General_6L_768` on `HotpotQA`.
| EM | F1 |
|------------|----------|
| 31.552419 | 53.535072 |
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-8_female-2_s226
|
jonatasgrosman
| 2022-07-25T09:42:46Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T09:42:24Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-8_female-2_s226
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-8_female-2_s156
|
jonatasgrosman
| 2022-07-25T09:37:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T09:37:36Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-8_female-2_s156
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
osanseviero/swin_unetr_btcv_segmentation
|
osanseviero
| 2022-07-25T09:27:25Z | 0 | 1 | null |
[
"MONAI",
"arxiv:2201.01266",
"arxiv:2111.14791",
"region:us"
] | null | 2022-07-25T09:27:20Z |
---
tags:
- MONAI
---
# Description
A pre-trained model for volumetric (3D) multi-organ segmentation from CT image.
# Model Overview
A pre-trained Swin UNETR [1,2] for volumetric (3D) multi-organ segmentation using CT images from Beyond the Cranial Vault (BTCV) Segmentation Challenge dataset [3].
## Data
The training data is from the [BTCV dataset](https://www.synapse.org/#!Synapse:syn3193805/wiki/89480/) (Please regist in `Synapse` and download the `Abdomen/RawData.zip`).
The dataset format needs to be redefined using the following commands:
```
unzip RawData.zip
mv RawData/Training/img/ RawData/imagesTr
mv RawData/Training/label/ RawData/labelsTr
mv RawData/Testing/img/ RawData/imagesTs
```
- Target: Multi-organs
- Task: Segmentation
- Modality: CT
- Size: 30 3D volumes (24 Training + 6 Testing)
## Training configuration
The training was performed with at least 32GB-memory GPUs.
Actual Model Input: 96 x 96 x 96
## Input and output formats
Input: 1 channel CT image
Output: 14 channels: 0:Background, 1:Spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland
## Performance
A graph showing the validation mean Dice for 5000 epochs.
 <br>
This model achieves the following Dice score on the validation data (our own split from the training dataset):
Mean Dice = 0.8283
Note that mean dice is computed in the original spacing of the input data.
## commands example
Execute training:
```
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
```
Override the `train` config to execute multi-GPU training:
```
torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
```
Override the `train` config to execute evaluation with the trained model:
```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
```
Execute inference:
```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
```
Export checkpoint to TorchScript file:
TorchScript conversion is currently not supported.
# Disclaimer
This is an example, not to be used for diagnostic purposes.
# References
[1] Hatamizadeh, Ali, et al. "Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images." arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.
[2] Tang, Yucheng, et al. "Self-supervised pre-training of swin transformers for 3d medical image analysis." arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791.
[3] Landman B, et al. "MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge." In Proc. of the MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge 2015 Oct (Vol. 5, p. 12).
|
thu-coai/EVA1.0
|
thu-coai
| 2022-07-25T09:17:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"zh",
"arxiv:2108.01547",
"arxiv:2203.09313",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T07:30:01Z |
---
language: zh
tags:
- pytorch
license: mit
---
# EVA
## Model Description
EVA is the largest open-source Chinese dialogue model with up to 2.8B parameters. The 1.0 version model is pre-trained on [WudaoCorpus-Dialog](https://resource.wudaoai.cn/home), and the 2.0 version is pre-trained on a carefully cleaned version of WudaoCorpus-Dialog which yields better performance than the 1.0 version. [Paper link](https://arxiv.org/abs/2108.01547) of EVA1.0. [Paper link](https://arxiv.org/abs/2203.09313) of EVA2.0.
## Model Configuration
| Model | n_params | n_enc-layers | n_dec-layers | d_model | d_ff | n_heads | d_head | attn-scale |
| ------------- | -------- | ------------ | ------------ | ------- | ----- | ------- | ------ | ---------- |
| EVA1.0 | 2.8B | 24 | 24 | 2,048 | 5,120 | 32 | 64 | No |
| EVA2.0_Base | 300M | 12 | 12 | 768 | 3,072 | 12 | 64 | Yes |
| EVA2.0_Large | 970M | 24 | 24 | 1,024 | 4,096 | 16 | 64 | Yes |
| EVA2.0_xLarge | 2.8B | 24 | 24 | 2,048 | 5,120 | 32 | 64 | Yes |
## How to use
Please refer to our GitHub [repository](https://github.com/thu-coai/EVA/tree/huggingface).
## Performance
We use the KdConv dataset to finetune and evaluate the model. Under the default hyperparameters in the scripts, we get the following results on the test set:
| | | | | |
| ---- | ---- | ---- | ---- | ---- |
| | loss | BLEU-4 | Dist-4 | f1 |
| eva1.0 Before finetune | 3.49 | 0.64 | 85.94 | 13.24 |
| eva1.0 After finetune | 2.37 | 3.94 | 72.47 | 19.80 |
| eva2.0 Before finetune | 3.14 | 1.02 | 82.67 | 14.36 |
| eva2.0 After finetune | 2.09 | 4.69 | 73.03 | 22.91 |
We also use beam search to run the interactive inference of EVA2.0:
```[]
Usr >>> 你好
Sys >>> 你好，请问你是？
Usr >>> 我是小明
Sys >>> 好的，小明，我是新来的，请多关照
Usr >>> 你了解人工智能吗？
Sys >>> 不了解，我只知道我是机器人
Usr >>> 那你很聪明
Sys >>> 谢谢夸奖，我会继续努力的
Usr >>> 你是学什么专业的呀
Sys >>> 计算机科学与技术，你呢？
Usr >>> 我也是学计算机的
Sys >>> 哦，那你有什么想知道的，可以问我
Usr >>> 编程语言你学过哪些？
Sys >>> java,c,c+都学过
Usr >>> 你学的真多
Sys >>> 呵呵，还好吧，我比较喜欢写代码
Usr >>> 哈哈，我也喜欢写代码，以后可以多多交流
Sys >>> 嗯，好的
Usr >>> 拜拜喽
Sys >>> 拜～
```
**NOET**: Since different hardware may have different implementations of random functions, even if you use the same random seed as ours, you may not be able to reproduce this case. But the overall performance will not differ much.
## Disclaimer
The pre-trained models aim to facilitate the research for conversation generation. The model provided in this repository is trained on a large dataset collected from various sources. Although a rigorous cleaning and filtering process has been carried out to the data and the model output, there is no guarantee that all the inappropriate contents have been completely banned. All the contents generated by the model do not represent the authors' opinions. The decoding script provided in this repository is only for research purposes. We are not responsible for any content generated using our model.
## Citation
```
@article{coai2021eva,
title={EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training},
author={Zhou, Hao and Ke, Pei and Zhang, Zheng and Gu, Yuxian and Zheng, Yinhe and Zheng, Chujie and Wang, Yida and Wu, Chen Henry and Sun, Hao and Yang, Xiaocong and Wen, Bosi and Zhu, Xiaoyan and Huang, Minlie and Tang, Jie},
journal={arXiv preprint arXiv:2108.01547},
year={2021}
}
@article{coai2022eva2,
title={{EVA2.0}: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training},
author={Gu, Yuxian and Wen, Jiaxin and Sun, Hao and Song, Yi and Ke, Pei and Zheng, Chujie and Zhang, Zheng and Yao, Jianzhu and Zhu, Xiaoyan and Tang, Jie and Huang, Minlie},
journal={arXiv preprint arXiv:2203.09313},
year={2022}
}
```
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-10_female-0_s246
|
jonatasgrosman
| 2022-07-25T09:13:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T09:12:53Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-10_female-0_s246
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-0_female-10_s878
|
jonatasgrosman
| 2022-07-25T09:03:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T09:03:23Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-0_female-10_s878
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-0_female-10_s496
|
jonatasgrosman
| 2022-07-25T08:58:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:58:04Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-0_female-10_s496
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-5_female-5_s966
|
jonatasgrosman
| 2022-07-25T08:48:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:48:38Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-5_female-5_s966
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_gender_male-5_female-5_s358
|
jonatasgrosman
| 2022-07-25T08:44:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:44:03Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_gender_male-5_female-5_s358
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-8_nortepeninsular-2_s149
|
jonatasgrosman
| 2022-07-25T08:29:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:29:02Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-8_nortepeninsular-2_s149
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
amartyobanerjee/bert-finetuned-ner
|
amartyobanerjee
| 2022-07-25T08:27:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-25T06:57:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9314097279472382
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.94095111185142
- name: Accuracy
type: accuracy
value: 0.9862541943839407
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0622
- Precision: 0.9314
- Recall: 0.9507
- F1: 0.9410
- Accuracy: 0.9863
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0821 | 1.0 | 1756 | 0.0639 | 0.9108 | 0.9371 | 0.9238 | 0.9834 |
| 0.0366 | 2.0 | 3512 | 0.0585 | 0.9310 | 0.9497 | 0.9403 | 0.9857 |
| 0.019 | 3.0 | 5268 | 0.0622 | 0.9314 | 0.9507 | 0.9410 | 0.9863 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-8_nortepeninsular-2_s1
|
jonatasgrosman
| 2022-07-25T08:23:58Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:23:47Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-8_nortepeninsular-2_s1
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s646
|
jonatasgrosman
| 2022-07-25T08:19:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:19:08Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s646
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s578
|
jonatasgrosman
| 2022-07-25T08:14:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:14:29Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s578
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s317
|
jonatasgrosman
| 2022-07-25T08:09:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:09:46Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-2_nortepeninsular-8_s317
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-10_nortepeninsular-0_s335
|
jonatasgrosman
| 2022-07-25T08:05:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:05:07Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-10_nortepeninsular-0_s335
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-10_nortepeninsular-0_s27
|
jonatasgrosman
| 2022-07-25T08:00:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T08:00:07Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-10_nortepeninsular-0_s27
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-0_nortepeninsular-10_s211
|
jonatasgrosman
| 2022-07-25T07:38:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T07:38:05Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-0_nortepeninsular-10_s211
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean
|
nlp-esg-scoring
| 2022-07-25T07:29:45Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-25T01:48:03Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nlp-esg-scoring/bert-base-finetuned-esg-TCFD-clean
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7816
- Validation Loss: 2.3592
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -571, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7776 | 2.3647 | 0 |
| 2.7744 | 2.3469 | 1 |
| 2.7683 | 2.3527 | 2 |
| 2.7743 | 2.3708 | 3 |
| 2.7809 | 2.3819 | 4 |
| 2.7674 | 2.3599 | 5 |
| 2.7715 | 2.3541 | 6 |
| 2.7766 | 2.3423 | 7 |
| 2.7834 | 2.3535 | 8 |
| 2.7816 | 2.3592 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Migga/ViT-BERT-Chess-V2
|
Migga
| 2022-07-25T07:28:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2022-07-25T05:59:53Z |
---
tags:
- generated_from_trainer
model-index:
- name: ViT-BERT-Chess-V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ViT-BERT-Chess-V2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7128
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0385 | 1.0 | 2770 | 3.9132 |
| 3.7453 | 2.0 | 5540 | 3.7552 |
| 3.6513 | 3.0 | 8310 | 3.7128 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2r_es_vp-100k_accent_surpeninsular-5_nortepeninsular-5_s324
|
jonatasgrosman
| 2022-07-25T07:22:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T07:22:31Z |
---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- es
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_es_vp-100k_accent_surpeninsular-5_nortepeninsular-5_s324
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
keithhon/nllb-200-3.3B
|
keithhon
| 2022-07-25T07:20:38Z | 5 | 3 |
transformers
|
[
"transformers",
"m2m_100",
"text2text-generation",
"nllb",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",
"crh",
"cy",
"da",
"de",
"dik",
"dyu",
"dz",
"el",
"en",
"eo",
"et",
"eu",
"ee",
"fo",
"fj",
"fi",
"fon",
"fr",
"fur",
"fuv",
"gaz",
"gd",
"ga",
"gl",
"gn",
"gu",
"ht",
"ha",
"he",
"hi",
"hne",
"hr",
"hu",
"hy",
"ig",
"ilo",
"id",
"is",
"it",
"jv",
"ja",
"kab",
"kac",
"kam",
"kn",
"ks",
"ka",
"kk",
"kbp",
"kea",
"khk",
"km",
"ki",
"rw",
"ky",
"kmb",
"kmr",
"knc",
"kg",
"ko",
"lo",
"lij",
"li",
"ln",
"lt",
"lmo",
"ltg",
"lb",
"lua",
"lg",
"luo",
"lus",
"lvs",
"mag",
"mai",
"ml",
"mar",
"min",
"mk",
"mt",
"mni",
"mos",
"mi",
"my",
"nl",
"nn",
"nb",
"npi",
"nso",
"nus",
"ny",
"oc",
"ory",
"pag",
"pa",
"pap",
"pbt",
"pes",
"plt",
"pl",
"pt",
"prs",
"quy",
"ro",
"rn",
"ru",
"sg",
"sa",
"sat",
"scn",
"shn",
"si",
"sk",
"sl",
"sm",
"sn",
"sd",
"so",
"st",
"es",
"sc",
"sr",
"ss",
"su",
"sv",
"swh",
"szl",
"ta",
"taq",
"tt",
"te",
"tg",
"tl",
"th",
"ti",
"tpi",
"tn",
"ts",
"tk",
"tum",
"tr",
"tw",
"tzm",
"ug",
"uk",
"umb",
"ur",
"uzn",
"vec",
"vi",
"war",
"wo",
"xh",
"ydd",
"yo",
"yue",
"zh",
"zsm",
"zu",
"dataset:flores-200",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-25T06:52:36Z |
---
language:
- ace
- acm
- acq
- aeb
- af
- ajp
- ak
- als
- am
- apc
- ar
- ars
- ary
- arz
- as
- ast
- awa
- ayr
- azb
- azj
- ba
- bm
- ban
- be
- bem
- bn
- bho
- bjn
- bo
- bs
- bug
- bg
- ca
- ceb
- cs
- cjk
- ckb
- crh
- cy
- da
- de
- dik
- dyu
- dz
- el
- en
- eo
- et
- eu
- ee
- fo
- fj
- fi
- fon
- fr
- fur
- fuv
- gaz
- gd
- ga
- gl
- gn
- gu
- ht
- ha
- he
- hi
- hne
- hr
- hu
- hy
- ig
- ilo
- id
- is
- it
- jv
- ja
- kab
- kac
- kam
- kn
- ks
- ka
- kk
- kbp
- kea
- khk
- km
- ki
- rw
- ky
- kmb
- kmr
- knc
- kg
- ko
- lo
- lij
- li
- ln
- lt
- lmo
- ltg
- lb
- lua
- lg
- luo
- lus
- lvs
- mag
- mai
- ml
- mar
- min
- mk
- mt
- mni
- mos
- mi
- my
- nl
- nn
- nb
- npi
- nso
- nus
- ny
- oc
- ory
- pag
- pa
- pap
- pbt
- pes
- plt
- pl
- pt
- prs
- quy
- ro
- rn
- ru
- sg
- sa
- sat
- scn
- shn
- si
- sk
- sl
- sm
- sn
- sd
- so
- st
- es
- sc
- sr
- ss
- su
- sv
- swh
- szl
- ta
- taq
- tt
- te
- tg
- tl
- th
- ti
- tpi
- tn
- ts
- tk
- tum
- tr
- tw
- tzm
- ug
- uk
- umb
- ur
- uzn
- vec
- vi
- war
- wo
- xh
- ydd
- yo
- yue
- zh
- zsm
- zu
language_details: "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn"
tags:
- nllb
license: "cc-by-nc-4.0"
datasets:
- flores-200
metrics:
- bleu
- spbleu
- chrf++
---
# NLLB-200
This is the model card of NLLB-200's 3.3B variant.
Here are the [metrics](https://tinyurl.com/nllb200dense3bmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper.
- Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022
- License: CC-BY-NC
- Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues
## Intended Use
- Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data.
- Primary intended users: Primary users are researchers and machine translation research community.
- Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
## Metrics
• Model performance measures: NLLB-200 model was evaluated using BLEU, spBLEU, and chrF++ metrics widely adopted by machine translation community. Additionally, we performed human evaluation with the XSTS protocol and measured the toxicity of the generated translations.
## Evaluation Data
- Datasets: Flores-200 dataset is described in Section 4
- Motivation: We used Flores-200 as it provides full evaluation coverage of the languages in NLLB-200
- Preprocessing: Sentence-split raw text data was preprocessed using SentencePiece. The
SentencePiece model is released along with NLLB-200.
## Training Data
• We used parallel multilingual data from a variety of sources to train the model. We provide detailed report on data selection and construction process in Section 5 in the paper. We also used monolingual data constructed from Common Crawl. We provide more details in Section 5.2.
## Ethical Considerations
• In this work, we took a reflexive approach in technological development to ensure that we prioritize human users and minimize risks that could be transferred to them. While we reflect on our ethical considerations throughout the article, here are some additional points to highlight. For one, many languages chosen for this study are low-resource languages, with a heavy emphasis on African languages. While quality translation could improve education and information access in many in these communities, such an access could also make groups with lower levels of digital literacy more vulnerable to misinformation or online scams. The latter scenarios could arise if bad actors misappropriate our work for nefarious activities, which we conceive as an example of unintended use. Regarding data acquisition, the training data used for model development were mined from various publicly available sources on the web. Although we invested heavily in data cleaning, personally identifiable information may not be entirely eliminated. Finally, although we did our best to optimize for translation quality, mistranslations produced by the model could remain. Although the odds are low, this could have adverse impact on those who rely on these translations to make important decisions (particularly when related to health and safety).
## Caveats and Recommendations
• Our model has been tested on the Wikimedia domain with limited investigation on other domains supported in NLLB-MD. In addition, the supported languages may have variations that our model is not capturing. Users should make appropriate assessments.
## Carbon Footprint Details
• The carbon dioxide (CO2e) estimate is reported in Section 8.8.
|
jonatasgrosman/exp_w2v2r_en_vp-100k_gender_male-8_female-2_s859
|
jonatasgrosman
| 2022-07-25T07:18:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T07:17:47Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_gender_male-8_female-2_s859
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
circulus/kobart-trans-gyeongsang-v1
|
circulus
| 2022-07-25T06:48:10Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-24T01:54:06Z |
KoBART 기반 경상도 사투리 스타일 변경
- AI-HUB 의 경상도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다.
|
circulus/kobart-trans-jeolla-v1
|
circulus
| 2022-07-25T06:47:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-24T02:02:09Z |
KoBART 기반 전라도 사투리 스타일 변경
- AI-HUB 의 전라도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다.
|
circulus/kobart-trans-chungcheong-v1
|
circulus
| 2022-07-25T06:47:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-24T02:04:57Z |
KoBART 기반 충청도 사투리 스타일 변경
- AI-HUB 의 충청도 사투리 데이터 셋을 통해 훈련되었습니다.
- 사용방법은 곧 올리도록 하겠습니다.
|
swtx/Erlangshen-Roberta-110M-Similarity
|
swtx
| 2022-07-25T06:46:00Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"NLU",
"NLI",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-25T06:22:56Z |
---
language:
- zh
license: apache-2.0
tags:
- bert
- NLU
- NLI
inference: true
widget:
- text: "今天心情不好[SEP]今天很开心"
---
# Erlangshen-Roberta-110M-Similarity, model (Chinese)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
We collect 20 paraphrace datasets in the Chinese domain for finetune, with a total of 2773880 samples. Our model is mainly based on [roberta](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
import torch
tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-Similarity')
texta='今天的饭不好吃'
textb='今天心情不好'
output=model(torch.tensor([tokenizer.encode(texta,textb)]))
print(torch.nn.functional.softmax(output.logits,dim=-1))
```
## Scores on downstream chinese tasks（The dev datasets of BUSTM and AFQMC may exist in the train set）
| Model | BQ | BUSTM | AFQMC |
| :--------: | :-----: | :----: | :-----: |
| Erlangshen-Roberta-110M-Similarity | 85.41 | 95.18 | 81.72 |
| Erlangshen-Roberta-330M-Similarity | 86.21 | 99.29 | 93.89 |
| Erlangshen-MegatronBert-1.3B-Similarity | 86.31 | - | - |
## Citation
If you find the resource is useful, please cite the following website in your paper.
```
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
huggingtweets/reillocity
|
huggingtweets
| 2022-07-25T06:40:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-12T23:14:20Z |
---
language: en
thumbnail: http://www.huggingtweets.com/reillocity/1658731242865/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1268284452586700800/BtFzXFsw_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Matt Collier</div>
<div style="text-align: center; font-size: 14px;">@reillocity</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Matt Collier.
| Data | Matt Collier |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 35 |
| Short tweets | 38 |
| Tweets kept | 3177 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20sr7og7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @reillocity's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3i5czu5f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3i5czu5f/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/reillocity')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
jonatasgrosman/exp_w2v2r_en_vp-100k_gender_male-10_female-0_s118
|
jonatasgrosman
| 2022-07-25T06:39:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T06:39:24Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_gender_male-10_female-0_s118
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_en_vp-100k_gender_male-0_female-10_s980
|
jonatasgrosman
| 2022-07-25T06:35:05Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T06:34:53Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_gender_male-0_female-10_s980
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_en_vp-100k_gender_male-0_female-10_s281
|
jonatasgrosman
| 2022-07-25T06:30:15Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T06:30:04Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_gender_male-0_female-10_s281
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_en_vp-100k_gender_male-5_female-5_s952
|
jonatasgrosman
| 2022-07-25T06:20:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T06:20:46Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_gender_male-5_female-5_s952
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bigmorning/distilgpt_new3_0080
|
bigmorning
| 2022-07-25T06:09:30Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-25T06:04:01Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0080
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0080
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4883
- Validation Loss: 2.3693
- Epoch: 79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
| 2.5189 | 2.4017 | 30 |
| 2.5182 | 2.4026 | 31 |
| 2.5176 | 2.4012 | 32 |
| 2.5168 | 2.4013 | 33 |
| 2.5161 | 2.3990 | 34 |
| 2.5154 | 2.3999 | 35 |
| 2.5149 | 2.3978 | 36 |
| 2.5142 | 2.3981 | 37 |
| 2.5135 | 2.3981 | 38 |
| 2.5130 | 2.3972 | 39 |
| 2.5123 | 2.3957 | 40 |
| 2.5116 | 2.3940 | 41 |
| 2.5108 | 2.3933 | 42 |
| 2.5103 | 2.3927 | 43 |
| 2.5095 | 2.3923 | 44 |
| 2.5090 | 2.3918 | 45 |
| 2.5083 | 2.3914 | 46 |
| 2.5078 | 2.3905 | 47 |
| 2.5070 | 2.3888 | 48 |
| 2.5062 | 2.3894 | 49 |
| 2.5058 | 2.3898 | 50 |
| 2.5051 | 2.3868 | 51 |
| 2.5045 | 2.3873 | 52 |
| 2.5041 | 2.3872 | 53 |
| 2.5035 | 2.3859 | 54 |
| 2.5027 | 2.3850 | 55 |
| 2.5020 | 2.3851 | 56 |
| 2.5016 | 2.3833 | 57 |
| 2.5009 | 2.3816 | 58 |
| 2.5002 | 2.3821 | 59 |
| 2.4995 | 2.3813 | 60 |
| 2.4990 | 2.3803 | 61 |
| 2.4984 | 2.3794 | 62 |
| 2.4977 | 2.3798 | 63 |
| 2.4971 | 2.3779 | 64 |
| 2.4964 | 2.3778 | 65 |
| 2.4959 | 2.3778 | 66 |
| 2.4954 | 2.3787 | 67 |
| 2.4947 | 2.3758 | 68 |
| 2.4942 | 2.3751 | 69 |
| 2.4935 | 2.3739 | 70 |
| 2.4929 | 2.3754 | 71 |
| 2.4923 | 2.3750 | 72 |
| 2.4918 | 2.3730 | 73 |
| 2.4912 | 2.3729 | 74 |
| 2.4906 | 2.3712 | 75 |
| 2.4901 | 2.3714 | 76 |
| 2.4894 | 2.3704 | 77 |
| 2.4888 | 2.3699 | 78 |
| 2.4883 | 2.3693 | 79 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2r_en_vp-100k_accent_us-8_england-2_s875
|
jonatasgrosman
| 2022-07-25T06:01:57Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T06:01:46Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_accent_us-8_england-2_s875
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_en_vp-100k_accent_us-2_england-8_s459
|
jonatasgrosman
| 2022-07-25T05:52:22Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T05:52:11Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_accent_us-2_england-8_s459
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_en_vp-100k_accent_us-2_england-8_s456
|
jonatasgrosman
| 2022-07-25T05:47:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T05:47:28Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_accent_us-2_england-8_s456
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
wuhuaguo/distilbert-base-uncased-finetuned-cola
|
wuhuaguo
| 2022-07-25T05:29:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-25T03:30:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5489250601752835
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8115
- Matthews Correlation: 0.5489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5223 | 1.0 | 535 | 0.5400 | 0.4165 |
| 0.349 | 2.0 | 1070 | 0.5125 | 0.4738 |
| 0.2392 | 3.0 | 1605 | 0.5283 | 0.5411 |
| 0.1791 | 4.0 | 2140 | 0.7506 | 0.5301 |
| 0.127 | 5.0 | 2675 | 0.8115 | 0.5489 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2r_en_vp-100k_accent_us-5_england-5_s924
|
jonatasgrosman
| 2022-07-25T05:07:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T05:07:18Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_en_vp-100k_accent_us-5_england-5_s924
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-8_female-2_s874
|
jonatasgrosman
| 2022-07-25T04:53:00Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:52:49Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-8_female-2_s874
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-8_female-2_s564
|
jonatasgrosman
| 2022-07-25T04:48:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:48:01Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-8_female-2_s564
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bigmorning/distilgpt_new3_0075
|
bigmorning
| 2022-07-25T04:44:50Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-25T04:39:38Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0075
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0075
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4912
- Validation Loss: 2.3729
- Epoch: 74
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
| 2.5189 | 2.4017 | 30 |
| 2.5182 | 2.4026 | 31 |
| 2.5176 | 2.4012 | 32 |
| 2.5168 | 2.4013 | 33 |
| 2.5161 | 2.3990 | 34 |
| 2.5154 | 2.3999 | 35 |
| 2.5149 | 2.3978 | 36 |
| 2.5142 | 2.3981 | 37 |
| 2.5135 | 2.3981 | 38 |
| 2.5130 | 2.3972 | 39 |
| 2.5123 | 2.3957 | 40 |
| 2.5116 | 2.3940 | 41 |
| 2.5108 | 2.3933 | 42 |
| 2.5103 | 2.3927 | 43 |
| 2.5095 | 2.3923 | 44 |
| 2.5090 | 2.3918 | 45 |
| 2.5083 | 2.3914 | 46 |
| 2.5078 | 2.3905 | 47 |
| 2.5070 | 2.3888 | 48 |
| 2.5062 | 2.3894 | 49 |
| 2.5058 | 2.3898 | 50 |
| 2.5051 | 2.3868 | 51 |
| 2.5045 | 2.3873 | 52 |
| 2.5041 | 2.3872 | 53 |
| 2.5035 | 2.3859 | 54 |
| 2.5027 | 2.3850 | 55 |
| 2.5020 | 2.3851 | 56 |
| 2.5016 | 2.3833 | 57 |
| 2.5009 | 2.3816 | 58 |
| 2.5002 | 2.3821 | 59 |
| 2.4995 | 2.3813 | 60 |
| 2.4990 | 2.3803 | 61 |
| 2.4984 | 2.3794 | 62 |
| 2.4977 | 2.3798 | 63 |
| 2.4971 | 2.3779 | 64 |
| 2.4964 | 2.3778 | 65 |
| 2.4959 | 2.3778 | 66 |
| 2.4954 | 2.3787 | 67 |
| 2.4947 | 2.3758 | 68 |
| 2.4942 | 2.3751 | 69 |
| 2.4935 | 2.3739 | 70 |
| 2.4929 | 2.3754 | 71 |
| 2.4923 | 2.3750 | 72 |
| 2.4918 | 2.3730 | 73 |
| 2.4912 | 2.3729 | 74 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-8_female-2_s129
|
jonatasgrosman
| 2022-07-25T04:43:26Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:43:14Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-8_female-2_s129
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-2_female-8_s364
|
jonatasgrosman
| 2022-07-25T04:38:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:38:41Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-2_female-8_s364
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-2_female-8_s211
|
jonatasgrosman
| 2022-07-25T04:33:58Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:33:47Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-2_female-8_s211
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-2_female-8_s108
|
jonatasgrosman
| 2022-07-25T04:29:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:28:54Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-2_female-8_s108
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-10_female-0_s75
|
jonatasgrosman
| 2022-07-25T04:24:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:23:59Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-10_female-0_s75
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-10_female-0_s504
|
jonatasgrosman
| 2022-07-25T04:19:07Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:18:55Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-10_female-0_s504
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-10_female-0_s325
|
jonatasgrosman
| 2022-07-25T04:14:27Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:14:16Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-10_female-0_s325
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-0_female-10_s801
|
jonatasgrosman
| 2022-07-25T04:04:30Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T04:04:16Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-0_female-10_s801
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_gender_male-0_female-10_s601
|
jonatasgrosman
| 2022-07-25T03:58:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T03:58:23Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_gender_male-0_female-10_s601
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
thu-coai/EVA2.0-large
|
thu-coai
| 2022-07-25T03:40:50Z | 2 | 3 |
transformers
|
[
"transformers",
"pytorch",
"zh",
"arxiv:2108.01547",
"arxiv:2203.09313",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T03:11:39Z |
---
language: zh
tags:
- pytorch
license: mit
---
# EVA
## Model Description
EVA is the largest open-source Chinese dialogue model with up to 2.8B parameters. The 1.0 version model is pre-trained on [WudaoCorpus-Dialog](https://resource.wudaoai.cn/home), and the 2.0 version is pre-trained on a carefully cleaned version of WudaoCorpus-Dialog which yields better performance than the 1.0 version. [Paper link](https://arxiv.org/abs/2108.01547) of EVA1.0. [Paper link](https://arxiv.org/abs/2203.09313) of EVA2.0.
## Model Configuration
| Model | n_params | n_enc-layers | n_dec-layers | d_model | d_ff | n_heads | d_head | attn-scale |
| ------------- | -------- | ------------ | ------------ | ------- | ----- | ------- | ------ | ---------- |
| EVA1.0 | 2.8B | 24 | 24 | 2,048 | 5,120 | 32 | 64 | No |
| EVA2.0_Base | 300M | 12 | 12 | 768 | 3,072 | 12 | 64 | Yes |
| EVA2.0_Large | 970M | 24 | 24 | 1,024 | 4,096 | 16 | 64 | Yes |
| EVA2.0_xLarge | 2.8B | 24 | 24 | 2,048 | 5,120 | 32 | 64 | Yes |
## How to use
Please refer to our GitHub [repository](https://github.com/thu-coai/EVA/tree/huggingface).
## Performance
We use the KdConv dataset to finetune and evaluate the model. Under the default hyperparameters in the scripts, we get the following results on the test set:
| | | | | |
| ---- | ---- | ---- | ---- | ---- |
| | loss | BLEU-4 | Dist-4 | f1 |
| eva1.0 Before finetune | 3.49 | 0.64 | 85.94 | 13.24 |
| eva1.0 After finetune | 2.37 | 3.94 | 72.47 | 19.80 |
| eva2.0 Before finetune | 3.14 | 1.02 | 82.67 | 14.36 |
| eva2.0 After finetune | 2.09 | 4.69 | 73.03 | 22.91 |
We also use beam search to run the interactive inference of EVA2.0:
```[]
Usr >>> 你好
Sys >>> 你好，请问你是？
Usr >>> 我是小明
Sys >>> 好的，小明，我是新来的，请多关照
Usr >>> 你了解人工智能吗？
Sys >>> 不了解，我只知道我是机器人
Usr >>> 那你很聪明
Sys >>> 谢谢夸奖，我会继续努力的
Usr >>> 你是学什么专业的呀
Sys >>> 计算机科学与技术，你呢？
Usr >>> 我也是学计算机的
Sys >>> 哦，那你有什么想知道的，可以问我
Usr >>> 编程语言你学过哪些？
Sys >>> java,c,c+都学过
Usr >>> 你学的真多
Sys >>> 呵呵，还好吧，我比较喜欢写代码
Usr >>> 哈哈，我也喜欢写代码，以后可以多多交流
Sys >>> 嗯，好的
Usr >>> 拜拜喽
Sys >>> 拜～
```
**NOET**: Since different hardware may have different implementations of random functions, even if you use the same random seed as ours, you may not be able to reproduce this case. But the overall performance will not differ much.
## Disclaimer
The pre-trained models aim to facilitate the research for conversation generation. The model provided in this repository is trained on a large dataset collected from various sources. Although a rigorous cleaning and filtering process has been carried out to the data and the model output, there is no guarantee that all the inappropriate contents have been completely banned. All the contents generated by the model do not represent the authors' opinions. The decoding script provided in this repository is only for research purposes. We are not responsible for any content generated using our model.
## Citation
```
@article{coai2021eva,
title={EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training},
author={Zhou, Hao and Ke, Pei and Zhang, Zheng and Gu, Yuxian and Zheng, Yinhe and Zheng, Chujie and Wang, Yida and Wu, Chen Henry and Sun, Hao and Yang, Xiaocong and Wen, Bosi and Zhu, Xiaoyan and Huang, Minlie and Tang, Jie},
journal={arXiv preprint arXiv:2108.01547},
year={2021}
}
@article{coai2022eva2,
title={{EVA2.0}: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training},
author={Gu, Yuxian and Wen, Jiaxin and Sun, Hao and Song, Yi and Ke, Pei and Zheng, Chujie and Zhang, Zheng and Yao, Jianzhu and Zhu, Xiaoyan and Tang, Jie and Huang, Minlie},
journal={arXiv preprint arXiv:2203.09313},
year={2022}
}
```
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-8_austria-2_s953
|
jonatasgrosman
| 2022-07-25T03:39:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T03:39:11Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-8_austria-2_s953
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
PlausiblePrediction/q-FrozenLake-v1-4x4-noSlippery
|
PlausiblePrediction
| 2022-07-25T03:38:31Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-25T03:38:25Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="nshenk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-8_austria-2_s807
|
jonatasgrosman
| 2022-07-25T03:34:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T03:34:27Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-8_austria-2_s807
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
bigmorning/distilgpt_new3_0070
|
bigmorning
| 2022-07-25T03:22:34Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-25T03:17:17Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0070
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0070
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4942
- Validation Loss: 2.3751
- Epoch: 69
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
| 2.5189 | 2.4017 | 30 |
| 2.5182 | 2.4026 | 31 |
| 2.5176 | 2.4012 | 32 |
| 2.5168 | 2.4013 | 33 |
| 2.5161 | 2.3990 | 34 |
| 2.5154 | 2.3999 | 35 |
| 2.5149 | 2.3978 | 36 |
| 2.5142 | 2.3981 | 37 |
| 2.5135 | 2.3981 | 38 |
| 2.5130 | 2.3972 | 39 |
| 2.5123 | 2.3957 | 40 |
| 2.5116 | 2.3940 | 41 |
| 2.5108 | 2.3933 | 42 |
| 2.5103 | 2.3927 | 43 |
| 2.5095 | 2.3923 | 44 |
| 2.5090 | 2.3918 | 45 |
| 2.5083 | 2.3914 | 46 |
| 2.5078 | 2.3905 | 47 |
| 2.5070 | 2.3888 | 48 |
| 2.5062 | 2.3894 | 49 |
| 2.5058 | 2.3898 | 50 |
| 2.5051 | 2.3868 | 51 |
| 2.5045 | 2.3873 | 52 |
| 2.5041 | 2.3872 | 53 |
| 2.5035 | 2.3859 | 54 |
| 2.5027 | 2.3850 | 55 |
| 2.5020 | 2.3851 | 56 |
| 2.5016 | 2.3833 | 57 |
| 2.5009 | 2.3816 | 58 |
| 2.5002 | 2.3821 | 59 |
| 2.4995 | 2.3813 | 60 |
| 2.4990 | 2.3803 | 61 |
| 2.4984 | 2.3794 | 62 |
| 2.4977 | 2.3798 | 63 |
| 2.4971 | 2.3779 | 64 |
| 2.4964 | 2.3778 | 65 |
| 2.4959 | 2.3778 | 66 |
| 2.4954 | 2.3787 | 67 |
| 2.4947 | 2.3758 | 68 |
| 2.4942 | 2.3751 | 69 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-2_austria-8_s732
|
jonatasgrosman
| 2022-07-25T03:20:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T03:20:30Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-2_austria-8_s732
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s545
|
jonatasgrosman
| 2022-07-25T03:06:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T03:05:59Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s545
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527
|
jonatasgrosman
| 2022-07-25T03:01:24Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T03:01:13Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-10_austria-0_s527
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
thu-coai/EVA2.0-xlarge
|
thu-coai
| 2022-07-25T02:57:30Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"zh",
"arxiv:2108.01547",
"arxiv:2203.09313",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-14T14:33:45Z |
---
language: zh
tags:
- pytorch
license: mit
---
# EVA
## Model Description
EVA is the largest open-source Chinese dialogue model with up to 2.8B parameters. The 1.0 version model is pre-trained on [WudaoCorpus-Dialog](https://resource.wudaoai.cn/home), and the 2.0 version is pre-trained on a carefully cleaned version of WudaoCorpus-Dialog which yields better performance than the 1.0 version. [Paper link](https://arxiv.org/abs/2108.01547) of EVA1.0. [Paper link](https://arxiv.org/abs/2203.09313) of EVA2.0.
## Model Configuration
| Model | n_params | n_enc-layers | n_dec-layers | d_model | d_ff | n_heads | d_head | attn-scale |
| ------------- | -------- | ------------ | ------------ | ------- | ----- | ------- | ------ | ---------- |
| EVA1.0 | 2.8B | 24 | 24 | 2,048 | 5,120 | 32 | 64 | No |
| EVA2.0_Base | 300M | 12 | 12 | 768 | 3,072 | 12 | 64 | Yes |
| EVA2.0_Large | 970M | 24 | 24 | 1,024 | 4,096 | 16 | 64 | Yes |
| EVA2.0_xLarge | 2.8B | 24 | 24 | 2,048 | 5,120 | 32 | 64 | Yes |
## How to use
Please refer to our GitHub [repository](https://github.com/thu-coai/EVA/tree/huggingface).
## Performance
We use the KdConv dataset to finetune and evaluate the model. Under the default hyperparameters in the scripts, we get the following results on the test set:
| | | | | |
| ---- | ---- | ---- | ---- | ---- |
| | loss | BLEU-4 | Dist-4 | f1 |
| eva1.0 Before finetune | 3.49 | 0.64 | 85.94 | 13.24 |
| eva1.0 After finetune | 2.37 | 3.94 | 72.47 | 19.80 |
| eva2.0 Before finetune | 3.14 | 1.02 | 82.67 | 14.36 |
| eva2.0 After finetune | 2.09 | 4.69 | 73.03 | 22.91 |
We also use beam search to run the interactive inference of EVA2.0:
```[]
Usr >>> 你好
Sys >>> 你好，请问你是？
Usr >>> 我是小明
Sys >>> 好的，小明，我是新来的，请多关照
Usr >>> 你了解人工智能吗？
Sys >>> 不了解，我只知道我是机器人
Usr >>> 那你很聪明
Sys >>> 谢谢夸奖，我会继续努力的
Usr >>> 你是学什么专业的呀
Sys >>> 计算机科学与技术，你呢？
Usr >>> 我也是学计算机的
Sys >>> 哦，那你有什么想知道的，可以问我
Usr >>> 编程语言你学过哪些？
Sys >>> java,c,c+都学过
Usr >>> 你学的真多
Sys >>> 呵呵，还好吧，我比较喜欢写代码
Usr >>> 哈哈，我也喜欢写代码，以后可以多多交流
Sys >>> 嗯，好的
Usr >>> 拜拜喽
Sys >>> 拜～
```
**NOET**: Since different hardware may have different implementations of random functions, even if you use the same random seed as ours, you may not be able to reproduce this case. But the overall performance will not differ much.
## Disclaimer
The pre-trained models aim to facilitate the research for conversation generation. The model provided in this repository is trained on a large dataset collected from various sources. Although a rigorous cleaning and filtering process has been carried out to the data and the model output, there is no guarantee that all the inappropriate contents have been completely banned. All the contents generated by the model do not represent the authors' opinions. The decoding script provided in this repository is only for research purposes. We are not responsible for any content generated using our model.
## Citation
```
@article{coai2021eva,
title={EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training},
author={Zhou, Hao and Ke, Pei and Zhang, Zheng and Gu, Yuxian and Zheng, Yinhe and Zheng, Chujie and Wang, Yida and Wu, Chen Henry and Sun, Hao and Yang, Xiaocong and Wen, Bosi and Zhu, Xiaoyan and Huang, Minlie and Tang, Jie},
journal={arXiv preprint arXiv:2108.01547},
year={2021}
}
@article{coai2022eva2,
title={{EVA2.0}: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training},
author={Gu, Yuxian and Wen, Jiaxin and Sun, Hao and Song, Yi and Ke, Pei and Zheng, Chujie and Zhang, Zheng and Yao, Jianzhu and Zhu, Xiaoyan and Tang, Jie and Huang, Minlie},
journal={arXiv preprint arXiv:2203.09313},
year={2022}
}
```
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756
|
jonatasgrosman
| 2022-07-25T02:56:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-25T02:56:00Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s756
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103
|
jonatasgrosman
| 2022-07-25T02:46:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"de",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-23T14:42:13Z |
---
language:
- de
license: apache-2.0
tags:
- automatic-speech-recognition
- de
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2r_de_vp-100k_accent_germany-0_austria-10_s103
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ben-yu/autotrain-MS2-1173943517
|
ben-yu
| 2022-07-25T01:31:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"led",
"text2text-generation",
"autotrain",
"unk",
"dataset:ben-yu/autotrain-data-MS2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-25T00:06:06Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- ben-yu/autotrain-data-MS2
co2_eq_emissions: 0.687008092853648
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1173943517
- CO2 Emissions (in grams): 0.687008092853648
## Validation Metrics
- Loss: 2.806302070617676
- Rouge1: 0.0342
- Rouge2: 0.006
- RougeL: 0.0242
- RougeLsum: 0.0283
- Gen Len: 19.9989
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/ben-yu/autotrain-MS2-1173943517
```
|
bigmorning/distilgpt_new3_0060
|
bigmorning
| 2022-07-25T00:37:57Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-25T00:32:14Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0060
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0060
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5002
- Validation Loss: 2.3821
- Epoch: 59
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
| 2.5189 | 2.4017 | 30 |
| 2.5182 | 2.4026 | 31 |
| 2.5176 | 2.4012 | 32 |
| 2.5168 | 2.4013 | 33 |
| 2.5161 | 2.3990 | 34 |
| 2.5154 | 2.3999 | 35 |
| 2.5149 | 2.3978 | 36 |
| 2.5142 | 2.3981 | 37 |
| 2.5135 | 2.3981 | 38 |
| 2.5130 | 2.3972 | 39 |
| 2.5123 | 2.3957 | 40 |
| 2.5116 | 2.3940 | 41 |
| 2.5108 | 2.3933 | 42 |
| 2.5103 | 2.3927 | 43 |
| 2.5095 | 2.3923 | 44 |
| 2.5090 | 2.3918 | 45 |
| 2.5083 | 2.3914 | 46 |
| 2.5078 | 2.3905 | 47 |
| 2.5070 | 2.3888 | 48 |
| 2.5062 | 2.3894 | 49 |
| 2.5058 | 2.3898 | 50 |
| 2.5051 | 2.3868 | 51 |
| 2.5045 | 2.3873 | 52 |
| 2.5041 | 2.3872 | 53 |
| 2.5035 | 2.3859 | 54 |
| 2.5027 | 2.3850 | 55 |
| 2.5020 | 2.3851 | 56 |
| 2.5016 | 2.3833 | 57 |
| 2.5009 | 2.3816 | 58 |
| 2.5002 | 2.3821 | 59 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigmorning/distilgpt_new3_0055
|
bigmorning
| 2022-07-24T23:14:10Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T23:08:24Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0055
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0055
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5035
- Validation Loss: 2.3859
- Epoch: 54
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
| 2.5189 | 2.4017 | 30 |
| 2.5182 | 2.4026 | 31 |
| 2.5176 | 2.4012 | 32 |
| 2.5168 | 2.4013 | 33 |
| 2.5161 | 2.3990 | 34 |
| 2.5154 | 2.3999 | 35 |
| 2.5149 | 2.3978 | 36 |
| 2.5142 | 2.3981 | 37 |
| 2.5135 | 2.3981 | 38 |
| 2.5130 | 2.3972 | 39 |
| 2.5123 | 2.3957 | 40 |
| 2.5116 | 2.3940 | 41 |
| 2.5108 | 2.3933 | 42 |
| 2.5103 | 2.3927 | 43 |
| 2.5095 | 2.3923 | 44 |
| 2.5090 | 2.3918 | 45 |
| 2.5083 | 2.3914 | 46 |
| 2.5078 | 2.3905 | 47 |
| 2.5070 | 2.3888 | 48 |
| 2.5062 | 2.3894 | 49 |
| 2.5058 | 2.3898 | 50 |
| 2.5051 | 2.3868 | 51 |
| 2.5045 | 2.3873 | 52 |
| 2.5041 | 2.3872 | 53 |
| 2.5035 | 2.3859 | 54 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
domenicrosati/deberta-v3-large-finetuned-synthetic-generated-only
|
domenicrosati
| 2022-07-24T22:50:35Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-24T13:50:55Z |
---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-finetuned-synthetic-generated-only
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large-finetuned-synthetic-generated-only
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0094
- F1: 0.9839
- Precision: 0.9849
- Recall: 0.9828
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.009 | 1.0 | 10387 | 0.0104 | 0.9722 | 0.9919 | 0.9533 |
| 0.0013 | 2.0 | 20774 | 0.0067 | 0.9825 | 0.9844 | 0.9805 |
| 0.0006 | 3.0 | 31161 | 0.0077 | 0.9843 | 0.9902 | 0.9786 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Billwzl/20split_dataset_version1
|
Billwzl
| 2022-07-24T20:49:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-21T07:44:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: 20split_dataset_version1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 20split_dataset_version1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.7475 | 1.0 | 11851 | 2.5194 |
| 2.5528 | 2.0 | 23702 | 2.4191 |
| 2.4649 | 3.0 | 35553 | 2.3646 |
| 2.4038 | 4.0 | 47404 | 2.3289 |
| 2.3632 | 5.0 | 59255 | 2.2922 |
| 2.3273 | 6.0 | 71106 | 2.2739 |
| 2.2964 | 7.0 | 82957 | 2.2494 |
| 2.2732 | 8.0 | 94808 | 2.2217 |
| 2.2526 | 9.0 | 106659 | 2.2149 |
| 2.2369 | 10.0 | 118510 | 2.2029 |
| 2.222 | 11.0 | 130361 | 2.2020 |
| 2.2135 | 12.0 | 142212 | 2.1942 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Chris1/reinforce-pixelcopter
|
Chris1
| 2022-07-24T20:27:28Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T15:43:10Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-pixelcopter
results:
- metrics:
- type: mean_reward
value: -2.60 +/- 1.56
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
pm390/a2c-AntBulletEnv-v0
|
pm390
| 2022-07-24T20:07:21Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T20:06:13Z |
---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 937.65 +/- 268.02
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sudo-s/modeversion2_m7_e8
|
sudo-s
| 2022-07-24T19:34:08Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-24T12:04:24Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: modeversion2_m7_e8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# modeversion2_m7_e8
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1060
- Accuracy: 0.9761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0231 | 0.06 | 100 | 3.8568 | 0.1883 |
| 3.3863 | 0.12 | 200 | 3.2510 | 0.2596 |
| 2.6187 | 0.18 | 300 | 2.6243 | 0.3882 |
| 2.3097 | 0.23 | 400 | 2.2189 | 0.4527 |
| 1.9016 | 0.29 | 500 | 1.9495 | 0.5244 |
| 1.7478 | 0.35 | 600 | 1.6609 | 0.6091 |
| 1.2345 | 0.41 | 700 | 1.4335 | 0.6426 |
| 1.4129 | 0.47 | 800 | 1.3001 | 0.6752 |
| 1.1722 | 0.53 | 900 | 1.2030 | 0.6785 |
| 1.0808 | 0.59 | 1000 | 1.0051 | 0.7273 |
| 0.8814 | 0.64 | 1100 | 1.0715 | 0.7063 |
| 0.9831 | 0.7 | 1200 | 0.9283 | 0.7334 |
| 0.8118 | 0.76 | 1300 | 0.8525 | 0.7631 |
| 0.7203 | 0.82 | 1400 | 0.7849 | 0.7756 |
| 0.8881 | 0.88 | 1500 | 0.8786 | 0.7487 |
| 0.6407 | 0.94 | 1600 | 0.6896 | 0.8000 |
| 0.7574 | 1.0 | 1700 | 0.7314 | 0.7754 |
| 0.6063 | 1.06 | 1800 | 0.6312 | 0.8068 |
| 0.4797 | 1.11 | 1900 | 0.5792 | 0.8296 |
| 0.4973 | 1.17 | 2000 | 0.5846 | 0.8221 |
| 0.4432 | 1.23 | 2100 | 0.7057 | 0.7905 |
| 0.5518 | 1.29 | 2200 | 0.5621 | 0.8304 |
| 0.3256 | 1.35 | 2300 | 0.5890 | 0.8143 |
| 0.4284 | 1.41 | 2400 | 0.5204 | 0.8485 |
| 0.3702 | 1.47 | 2500 | 0.5699 | 0.8256 |
| 0.2858 | 1.52 | 2600 | 0.5815 | 0.8287 |
| 0.3706 | 1.58 | 2700 | 0.4615 | 0.8571 |
| 0.3484 | 1.64 | 2800 | 0.4812 | 0.8518 |
| 0.2865 | 1.7 | 2900 | 0.4285 | 0.8638 |
| 0.4474 | 1.76 | 3000 | 0.5217 | 0.8377 |
| 0.2101 | 1.82 | 3100 | 0.4478 | 0.8589 |
| 0.3545 | 1.88 | 3200 | 0.4444 | 0.8612 |
| 0.2728 | 1.93 | 3300 | 0.4213 | 0.8645 |
| 0.3525 | 1.99 | 3400 | 0.3551 | 0.8848 |
| 0.0936 | 2.05 | 3500 | 0.4074 | 0.8748 |
| 0.2118 | 2.11 | 3600 | 0.4089 | 0.8812 |
| 0.2744 | 2.17 | 3700 | 0.3534 | 0.8894 |
| 0.211 | 2.23 | 3800 | 0.4422 | 0.8599 |
| 0.1684 | 2.29 | 3900 | 0.3705 | 0.8858 |
| 0.1885 | 2.34 | 4000 | 0.3651 | 0.8862 |
| 0.249 | 2.4 | 4100 | 0.4234 | 0.8687 |
| 0.1485 | 2.46 | 4200 | 0.3784 | 0.8798 |
| 0.1188 | 2.52 | 4300 | 0.3589 | 0.8873 |
| 0.1274 | 2.58 | 4400 | 0.3570 | 0.8917 |
| 0.2206 | 2.64 | 4500 | 0.3377 | 0.8920 |
| 0.1287 | 2.7 | 4600 | 0.3170 | 0.9023 |
| 0.1805 | 2.75 | 4700 | 0.3469 | 0.8934 |
| 0.1505 | 2.81 | 4800 | 0.4258 | 0.8757 |
| 0.1592 | 2.87 | 4900 | 0.3415 | 0.8948 |
| 0.1297 | 2.93 | 5000 | 0.3168 | 0.9028 |
| 0.1284 | 2.99 | 5100 | 0.3060 | 0.9089 |
| 0.0833 | 3.05 | 5200 | 0.2610 | 0.9207 |
| 0.0334 | 3.11 | 5300 | 0.2766 | 0.9197 |
| 0.0847 | 3.17 | 5400 | 0.3366 | 0.9016 |
| 0.1112 | 3.22 | 5500 | 0.3098 | 0.9079 |
| 0.0477 | 3.28 | 5600 | 0.3385 | 0.9041 |
| 0.0419 | 3.34 | 5700 | 0.2944 | 0.9139 |
| 0.0827 | 3.4 | 5800 | 0.2715 | 0.9239 |
| 0.0659 | 3.46 | 5900 | 0.2695 | 0.9230 |
| 0.0244 | 3.52 | 6000 | 0.3050 | 0.9147 |
| 0.0883 | 3.58 | 6100 | 0.2862 | 0.9203 |
| 0.0527 | 3.63 | 6200 | 0.2383 | 0.9319 |
| 0.0828 | 3.69 | 6300 | 0.2984 | 0.9182 |
| 0.0678 | 3.75 | 6400 | 0.2135 | 0.9436 |
| 0.0492 | 3.81 | 6500 | 0.2605 | 0.9296 |
| 0.0374 | 3.87 | 6600 | 0.2192 | 0.9380 |
| 0.1846 | 3.93 | 6700 | 0.2804 | 0.9187 |
| 0.0557 | 3.99 | 6800 | 0.2599 | 0.9253 |
| 0.0127 | 4.04 | 6900 | 0.2412 | 0.9336 |
| 0.0203 | 4.1 | 7000 | 0.2214 | 0.9415 |
| 0.0272 | 4.16 | 7100 | 0.2322 | 0.9356 |
| 0.066 | 4.22 | 7200 | 0.2643 | 0.9325 |
| 0.0628 | 4.28 | 7300 | 0.2170 | 0.9406 |
| 0.0108 | 4.34 | 7400 | 0.2388 | 0.9405 |
| 0.026 | 4.4 | 7500 | 0.2533 | 0.9372 |
| 0.0401 | 4.45 | 7600 | 0.2407 | 0.9358 |
| 0.0493 | 4.51 | 7700 | 0.2213 | 0.9415 |
| 0.0951 | 4.57 | 7800 | 0.3016 | 0.9237 |
| 0.0017 | 4.63 | 7900 | 0.2183 | 0.9448 |
| 0.0561 | 4.69 | 8000 | 0.1962 | 0.9492 |
| 0.0063 | 4.75 | 8100 | 0.1868 | 0.9522 |
| 0.0054 | 4.81 | 8200 | 0.2068 | 0.9459 |
| 0.0519 | 4.87 | 8300 | 0.2141 | 0.9429 |
| 0.027 | 4.92 | 8400 | 0.2138 | 0.9438 |
| 0.0034 | 4.98 | 8500 | 0.1774 | 0.9529 |
| 0.0096 | 5.04 | 8600 | 0.1778 | 0.9512 |
| 0.0011 | 5.1 | 8700 | 0.1854 | 0.9512 |
| 0.0195 | 5.16 | 8800 | 0.1914 | 0.9483 |
| 0.0245 | 5.22 | 8900 | 0.2156 | 0.9471 |
| 0.0055 | 5.28 | 9000 | 0.1640 | 0.9574 |
| 0.0166 | 5.33 | 9100 | 0.1770 | 0.9568 |
| 0.0217 | 5.39 | 9200 | 0.2011 | 0.9479 |
| 0.0017 | 5.45 | 9300 | 0.2210 | 0.9462 |
| 0.0161 | 5.51 | 9400 | 0.1510 | 0.9621 |
| 0.0193 | 5.57 | 9500 | 0.1643 | 0.9586 |
| 0.0121 | 5.63 | 9600 | 0.1716 | 0.9535 |
| 0.0146 | 5.69 | 9700 | 0.1720 | 0.9554 |
| 0.0071 | 5.74 | 9800 | 0.1831 | 0.9541 |
| 0.0018 | 5.8 | 9900 | 0.2076 | 0.9485 |
| 0.0007 | 5.86 | 10000 | 0.1636 | 0.9599 |
| 0.0005 | 5.92 | 10100 | 0.1625 | 0.9602 |
| 0.0277 | 5.98 | 10200 | 0.1874 | 0.9546 |
| 0.0005 | 6.04 | 10300 | 0.1790 | 0.9579 |
| 0.0012 | 6.1 | 10400 | 0.1840 | 0.9544 |
| 0.0431 | 6.15 | 10500 | 0.1571 | 0.9628 |
| 0.0332 | 6.21 | 10600 | 0.1599 | 0.9591 |
| 0.0014 | 6.27 | 10700 | 0.1493 | 0.9632 |
| 0.0014 | 6.33 | 10800 | 0.1366 | 0.9661 |
| 0.0006 | 6.39 | 10900 | 0.1582 | 0.9609 |
| 0.0005 | 6.45 | 11000 | 0.1704 | 0.9589 |
| 0.0004 | 6.51 | 11100 | 0.1376 | 0.9671 |
| 0.0755 | 6.57 | 11200 | 0.1375 | 0.9654 |
| 0.0002 | 6.62 | 11300 | 0.1361 | 0.9661 |
| 0.0006 | 6.68 | 11400 | 0.1323 | 0.9675 |
| 0.0009 | 6.74 | 11500 | 0.1239 | 0.9692 |
| 0.0004 | 6.8 | 11600 | 0.1514 | 0.9631 |
| 0.0002 | 6.86 | 11700 | 0.1386 | 0.9664 |
| 0.0004 | 6.92 | 11800 | 0.1368 | 0.9659 |
| 0.0004 | 6.98 | 11900 | 0.1276 | 0.9684 |
| 0.0002 | 7.03 | 12000 | 0.1171 | 0.9712 |
| 0.0002 | 7.09 | 12100 | 0.1142 | 0.9711 |
| 0.0001 | 7.15 | 12200 | 0.1183 | 0.9727 |
| 0.0002 | 7.21 | 12300 | 0.1167 | 0.9732 |
| 0.0002 | 7.27 | 12400 | 0.1143 | 0.9737 |
| 0.0001 | 7.33 | 12500 | 0.1129 | 0.9737 |
| 0.0002 | 7.39 | 12600 | 0.1116 | 0.9742 |
| 0.0002 | 7.44 | 12700 | 0.1126 | 0.9745 |
| 0.0002 | 7.5 | 12800 | 0.1111 | 0.9748 |
| 0.0002 | 7.56 | 12900 | 0.1102 | 0.9747 |
| 0.0001 | 7.62 | 13000 | 0.1094 | 0.9747 |
| 0.0001 | 7.68 | 13100 | 0.1086 | 0.9742 |
| 0.0001 | 7.74 | 13200 | 0.1079 | 0.9748 |
| 0.0002 | 7.8 | 13300 | 0.1062 | 0.9754 |
| 0.0002 | 7.85 | 13400 | 0.1068 | 0.9757 |
| 0.0001 | 7.91 | 13500 | 0.1061 | 0.9762 |
| 0.0001 | 7.97 | 13600 | 0.1060 | 0.9761 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sushrut58/my-finetuned-t5
|
sushrut58
| 2022-07-24T19:13:30Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-24T19:13:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: my-finetuned-t5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# my-finetuned-t5
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jakka/dqn-SpaceInvadersNoFrameskip-v4_1
|
jakka
| 2022-07-24T19:03:29Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T19:02:51Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 769.00 +/- 232.34
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jakka -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jakka
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
bigmorning/distilgpt_new3_0040
|
bigmorning
| 2022-07-24T18:51:53Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-24T18:46:05Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_new3_0040
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilgpt_new3_0040
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5130
- Validation Loss: 2.3972
- Epoch: 39
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.5407 | 2.4254 | 0 |
| 2.5399 | 2.4247 | 1 |
| 2.5391 | 2.4238 | 2 |
| 2.5383 | 2.4232 | 3 |
| 2.5375 | 2.4210 | 4 |
| 2.5368 | 2.4210 | 5 |
| 2.5361 | 2.4197 | 6 |
| 2.5353 | 2.4193 | 7 |
| 2.5345 | 2.4191 | 8 |
| 2.5339 | 2.4177 | 9 |
| 2.5332 | 2.4188 | 10 |
| 2.5324 | 2.4160 | 11 |
| 2.5317 | 2.4164 | 12 |
| 2.5309 | 2.4145 | 13 |
| 2.5302 | 2.4153 | 14 |
| 2.5295 | 2.4139 | 15 |
| 2.5288 | 2.4134 | 16 |
| 2.5282 | 2.4123 | 17 |
| 2.5274 | 2.4116 | 18 |
| 2.5267 | 2.4110 | 19 |
| 2.5259 | 2.4106 | 20 |
| 2.5251 | 2.4097 | 21 |
| 2.5244 | 2.4074 | 22 |
| 2.5238 | 2.4078 | 23 |
| 2.5232 | 2.4072 | 24 |
| 2.5223 | 2.4062 | 25 |
| 2.5217 | 2.4054 | 26 |
| 2.5211 | 2.4057 | 27 |
| 2.5204 | 2.4044 | 28 |
| 2.5197 | 2.4026 | 29 |
| 2.5189 | 2.4017 | 30 |
| 2.5182 | 2.4026 | 31 |
| 2.5176 | 2.4012 | 32 |
| 2.5168 | 2.4013 | 33 |
| 2.5161 | 2.3990 | 34 |
| 2.5154 | 2.3999 | 35 |
| 2.5149 | 2.3978 | 36 |
| 2.5142 | 2.3981 | 37 |
| 2.5135 | 2.3981 | 38 |
| 2.5130 | 2.3972 | 39 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Amiri/1_land_on_the_moon_PPO
|
Amiri
| 2022-07-24T18:15:55Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T18:03:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 65.60 +/- 85.56
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
nateraw/my-aurora
|
nateraw
| 2022-07-24T17:53:19Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"🧨 Diffuse It",
"en",
"dataset:aurora",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-07-24T17:32:57Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags:
- "\U0001F9E8 Diffuse It"
datasets: aurora
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# my-aurora
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `aurora` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/nateraw/my-aurora/tensorboard?#scalars)
|
jakka/q-Taxi-v3
|
jakka
| 2022-07-24T16:09:36Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-24T16:09:31Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="jakka/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
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