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text-classification | transformers |
# xlm-r-finetuned-toxic-political-tweets-es
This model is based on the pre-trained model [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) and was fine-tuned on a dataset of tweets from members of the [Spanish Congress of the Deputies](https://www.congreso.es/) annotated regarding the level of political toxicity they generate.
### Inputs
The model has been trained on the text of Spanish tweets authored by politicians in 2021, so this is the input expected and its performance can degrade when applied to texts from other domains.
### Outputs
The model predicts 2 signals of political toxicity:
* Toxic: the tweet has at least some degree of toxicity.
* Very Toxic: the tweet has a strong degree of toxicity.
A value between 0 and 1 is predicted for each signal.
### Intended uses & limitations
The model was created to be used as a toxicity detector of spanish tweets from Spanish Congress Deputies. If the intended use is other one, for instance; toxicity detection on films reviews, the results won't be reliable and you might look for another model with this concrete purpose.
### How to use
The model can be used directly with a text-classification pipeline:
```python
>>> from transformers import pipeline
>>> text = "Es usted un auténtico impresentable, su señoría."
>>> pipe = pipeline("text-classification", model="Newtral/xlm-r-finetuned-toxic-political-tweets-es")
>>> pipe(text, return_all_scores=True)
[[{'label': 'toxic', 'score': 0.92560875415802},
{'label': 'very toxic', 'score': 0.8310967683792114}]]
```
### Training procedure
The pre-trained model was fine-tuned for sequence classification using the following hyperparameters, which were selected from a validation set:
* Batch size = 32
* Learning rate = 2e-5
* Epochs = 5
* Max length = 64
The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance. | {"language": "es", "license": "apache-2.0"} | Newtral/xlm-r-finetuned-toxic-political-tweets-es | null | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Nezz222/Nerezz | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | NhatPham/my-new-shiny-tokenizer | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ## labels
- 0: Object
- 1: Recycle
- 2: Non-Recycle
# vit-base-patch16-224
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1510
- Accuracy: 0.9443
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1438 | 1.0 | 150 | 0.1645 | 0.9353 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224", "results": []}]} | NhatPham/vit-base-patch16-224-recylce-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1258
- Accuracy: 0.9793
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1561 | 1.0 | 399 | 1.1127 | 0.6643 |
| 0.4803 | 2.0 | 798 | 0.3547 | 0.9687 |
| 0.2855 | 3.0 | 1197 | 0.1663 | 0.9763 |
| 0.1987 | 4.0 | 1596 | 0.1258 | 0.9793 |
| 0.2097 | 5.0 | 1995 | 0.1171 | 0.9791 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-finetuned-ks", "results": []}]} | NhatPham/wav2vec2-base-finetuned-ks | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
# wav2vec2-large-xlsr-53-french
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in French using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "fr", split="test[:20%]")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the French test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "fr")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\â€\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 29.31 %
## Training
V1 of the Common Voice `train`, `validation` datasets were used for training.
## Testing
20% of V6.1 of the Common Voice `Test` dataset were used for training. | {"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-French by Nhut DOAN NGUYEN", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice fr", "type": "common_voice", "args": "fr"}, "metrics": [{"type": "wer", "value": "xx.xx", "name": "Test WER"}]}]}]} | Nhut/wav2vec2-large-xlsr-french | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"fr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co/datasets/common_voice), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VIVOS](https://ailab.hcmus.edu.vn/vivos).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
ENCODER = {
"ia ": "iê ",
"ìa ": "iề ",
"ía ": "iế ",
"ỉa ": "iể ",
"ĩa ": "iễ ",
"ịa ": "iệ ",
"ya ": "yê ",
"ỳa ": "yề ",
"ýa ": "yế ",
"ỷa ": "yể ",
"ỹa ": "yễ ",
"ỵa ": "yệ ",
"ua ": "uô ",
"ùa ": "uồ ",
"úa ": "uố ",
"ủa ": "uổ ",
"ũa ": "uỗ ",
"ụa ": "uộ ",
"ưa ": "ươ ",
"ừa ": "ườ ",
"ứa ": "ướ ",
"ửa ": "ưở ",
"ữa ": "ưỡ ",
"ựa ": "ượ ",
"ke": "ce",
"kè": "cè",
"ké": "cé",
"kẻ": "cẻ",
"kẽ": "cẽ",
"kẹ": "cẹ",
"kê": "cê",
"kề": "cề",
"kế": "cế",
"kể": "cể",
"kễ": "cễ",
"kệ": "cệ",
"ki": "ci",
"kì": "cì",
"kí": "cí",
"kỉ": "cỉ",
"kĩ": "cĩ",
"kị": "cị",
"ky": "cy",
"kỳ": "cỳ",
"ký": "cý",
"kỷ": "cỷ",
"kỹ": "cỹ",
"kỵ": "cỵ",
"ghe": "ge",
"ghè": "gè",
"ghé": "gé",
"ghẻ": "gẻ",
"ghẽ": "gẽ",
"ghẹ": "gẹ",
"ghê": "gê",
"ghề": "gề",
"ghế": "gế",
"ghể": "gể",
"ghễ": "gễ",
"ghệ": "gệ",
"ngh": "\x80",
"uyê": "\x96",
"uyề": "\x97",
"uyế": "\x98",
"uyể": "\x99",
"uyễ": "\x9a",
"uyệ": "\x9b",
"ng": "\x81",
"ch": "\x82",
"gh": "\x83",
"nh": "\x84",
"gi": "\x85",
"ph": "\x86",
"kh": "\x87",
"th": "\x88",
"tr": "\x89",
"uy": "\x8a",
"uỳ": "\x8b",
"uý": "\x8c",
"uỷ": "\x8d",
"uỹ": "\x8e",
"uỵ": "\x8f",
"iê": "\x90",
"iề": "\x91",
"iế": "\x92",
"iể": "\x93",
"iễ": "\x94",
"iệ": "\x95",
"uô": "\x9c",
"uồ": "\x9d",
"uố": "\x9e",
"uổ": "\x9f",
"uỗ": "\xa0",
"uộ": "\xa1",
"ươ": "\xa2",
"ườ": "\xa3",
"ướ": "\xa4",
"ưở": "\xa5",
"ưỡ": "\xa6",
"ượ": "\xa7",
}
def decode_string(x):
for k, v in list(reversed(list(ENCODER.items()))):
x = x.replace(v, k)
return x
test_dataset = load_dataset("common_voice", "vi", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", [decode_string(x) for x in processor.batch_decode(predicted_ids)])
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Vietnamese test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
ENCODER = {
"ia ": "iê ",
"ìa ": "iề ",
"ía ": "iế ",
"ỉa ": "iể ",
"ĩa ": "iễ ",
"ịa ": "iệ ",
"ya ": "yê ",
"ỳa ": "yề ",
"ýa ": "yế ",
"ỷa ": "yể ",
"ỹa ": "yễ ",
"ỵa ": "yệ ",
"ua ": "uô ",
"ùa ": "uồ ",
"úa ": "uố ",
"ủa ": "uổ ",
"ũa ": "uỗ ",
"ụa ": "uộ ",
"ưa ": "ươ ",
"ừa ": "ườ ",
"ứa ": "ướ ",
"ửa ": "ưở ",
"ữa ": "ưỡ ",
"ựa ": "ượ ",
"ke": "ce",
"kè": "cè",
"ké": "cé",
"kẻ": "cẻ",
"kẽ": "cẽ",
"kẹ": "cẹ",
"kê": "cê",
"kề": "cề",
"kế": "cế",
"kể": "cể",
"kễ": "cễ",
"kệ": "cệ",
"ki": "ci",
"kì": "cì",
"kí": "cí",
"kỉ": "cỉ",
"kĩ": "cĩ",
"kị": "cị",
"ky": "cy",
"kỳ": "cỳ",
"ký": "cý",
"kỷ": "cỷ",
"kỹ": "cỹ",
"kỵ": "cỵ",
"ghe": "ge",
"ghè": "gè",
"ghé": "gé",
"ghẻ": "gẻ",
"ghẽ": "gẽ",
"ghẹ": "gẹ",
"ghê": "gê",
"ghề": "gề",
"ghế": "gế",
"ghể": "gể",
"ghễ": "gễ",
"ghệ": "gệ",
"ngh": "\x80",
"uyê": "\x96",
"uyề": "\x97",
"uyế": "\x98",
"uyể": "\x99",
"uyễ": "\x9a",
"uyệ": "\x9b",
"ng": "\x81",
"ch": "\x82",
"gh": "\x83",
"nh": "\x84",
"gi": "\x85",
"ph": "\x86",
"kh": "\x87",
"th": "\x88",
"tr": "\x89",
"uy": "\x8a",
"uỳ": "\x8b",
"uý": "\x8c",
"uỷ": "\x8d",
"uỹ": "\x8e",
"uỵ": "\x8f",
"iê": "\x90",
"iề": "\x91",
"iế": "\x92",
"iể": "\x93",
"iễ": "\x94",
"iệ": "\x95",
"uô": "\x9c",
"uồ": "\x9d",
"uố": "\x9e",
"uổ": "\x9f",
"uỗ": "\xa0",
"uộ": "\xa1",
"ươ": "\xa2",
"ườ": "\xa3",
"ướ": "\xa4",
"ưở": "\xa5",
"ưỡ": "\xa6",
"ượ": "\xa7",
}
def decode_string(x):
for k, v in list(reversed(list(ENCODER.items()))):
x = x.replace(v, k)
return x
test_dataset = load_dataset("common_voice", "vi", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
model.to("cuda")
chars_to_ignore_regex = '[\\\+\@\ǀ\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
# decode_string: We replace the encoded letter with the initial letters
batch["pred_strings"] = [decode_string(x) for x in batch["pred_strings"]]
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 49.59 %
## Training
The Common Voice `train`, `validation` and FOSD datasets and VIVOS datasets were used for training as well.
The script used for training can be found [here](https://colab.research.google.com/drive/11pP4uVJj4SYZTzGjlCUtOHywlhYqs0cPx) | {"language": "vi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", {"FOSD": "https://data.mendeley.com/datasets/k9sxg2twv4/4"}, {"VIVOS": "https://ailab.hcmus.edu.vn/vivos"}], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Vietnamese by Nhut", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice vi", "type": "common_voice", "args": "vi"}, "metrics": [{"type": "wer", "value": 49.59, "name": "Test WER"}]}]}]} | Nhut/wav2vec2-large-xlsr-vietnamese | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"vi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | NibrasShami/DialopGPT-small-HarryPotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | keras | {} | Niciu/keras-dummy-functional-demo | null | [
"keras",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | keras | {} | Niciu/keras-dummy-model-mixin-demo | null | [
"keras",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | keras | {} | Niciu/keras-dummy-sequential-demo | null | [
"keras",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | this project was created to use in wav2vec | {} | Niciu/testtest1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Niciu/wav2vec2-base-timit-demo-colab | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Niciu/wav2vec2-base-timit-demo-colab1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Niciu/wav2vec2-base-timit-demo-colab2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Niciu/wav2vec2-base-vivo-demo-colab1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Niciu/wav2vec2-large-xlsr-thai-demo | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Niciu/wav2vectest1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nick96/B-A | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# My Awesome Laffy | {"tags": ["conversational"]} | NickCavarretta/DialoGPT-small-laffy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Nickis/distilgpt2-finetuned-wikitext2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nickis/distilroberta-base-finetuned-data | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nickis/distilroberta-base-finetuned-wikitext2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nickolay/sevbot | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4519
- Wer: 0.3375
## 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: 0.0001
- train_batch_size: 32
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4351 | 4.0 | 500 | 1.2740 | 0.8259 |
| 0.5828 | 8.0 | 1000 | 0.4276 | 0.4403 |
| 0.2274 | 12.0 | 1500 | 0.4646 | 0.3739 |
| 0.135 | 16.0 | 2000 | 0.4320 | 0.3662 |
| 0.0962 | 20.0 | 2500 | 0.4831 | 0.3607 |
| 0.0719 | 24.0 | 3000 | 0.4506 | 0.3463 |
| 0.0556 | 28.0 | 3500 | 0.4519 | 0.3375 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | NicoGrageda/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | {} | NicolasPeruchot/Biography | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Squi | {"tags": ["conversational"]} | Nihwy/DialoSqui | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Nikhil8800868912/wav2vec2-base-timit-demo-colab-new-ASR-wer | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | NikhilKrishna/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | NikhilRamesh/Fetch_Loc | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nikhilshandilya9/Unet | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers |
# **-- EMODa --**
## BERT-model for danish multi-class classification of emotions
Classifies a danish sentence into one of 6 different emotions:
| Danish emotion | Ekman's emotion |
| ----- | ----- |
| 😞 **Afsky** | Disgust |
| 😨 **Frygt** | Fear |
| 😄 **Glæde** | Joy |
| 😱 **Overraskelse** | Surprise |
| 😢 **Tristhed** | Sadness |
| 😠 **Vrede** | Anger |
# How to use
```python
from transformers import pipeline
model_path = "NikolajMunch/danish-emotion-classification"
classifier = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
prediction = classifier("Jeg er godt nok ked af at mine SMS'er er slettet")
print(prediction)
# [{'label': 'Tristhed', 'score': 0.9725030660629272}]
```
or
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("NikolajMunch/danish-emotion-classification")
model = AutoModelForSequenceClassification.from_pretrained("NikolajMunch/danish-emotion-classification")
```
| {"language": ["da"], "tags": ["sentiment", "emotion", "danish"], "widget": [{"text": "Hold da op! Kan det virkelig passe?"}]} | NikolajMunch/danish-emotion-classification | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentiment",
"emotion",
"danish",
"da",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers | {} | NikolajW/BaselineThesis | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nilav/layoutlmv2-finetuned-funsd-test | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | # AOT-GAN CelebA-HQ
AOT-GAN is a model that can be used for image in-painting. The CelebA-HQ checkpoint is trained on synthetic human faces, which should make it suitable for touching up and restoring portraits.
This model was generated using [AOT-GAN-for-Inpainting](https://github.com/researchmm/AOT-GAN-for-Inpainting), cited as
```
@inproceedings{yan2021agg,
author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining},
title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting},
booktitle = {Arxiv},
pages={-},
year = {2020}
}
```
## Dataset
The CelebA-HQ dataset was created with this codebase: https://github.com/tkarras/progressive_growing_of_gans, owned by NVidia and licensed under Creative Commons Attribution-NonCommercial 4.0 International. | {"tags": ["face-recognition", "face-generation", "face-segmentation", "generative-adversarial-network"], "datasets": ["celeba-hq"], "metrics": ["L1", "PSNR", "SSIM", "FID"]} | NimaBoscarino/aot-gan-celebahq | null | [
"transformers",
"pytorch",
"face-recognition",
"face-generation",
"face-segmentation",
"generative-adversarial-network",
"dataset:celeba-hq",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | transformers | # AOT-GAN Places2
AOT-GAN is a model that can be used for image in-painting. The Places2 checkpoint is trained on a dataset which should make it suitable for touching up and restoring images of landscapes, buildings, and other natural and developed places.
This model was generated using [AOT-GAN-for-Inpainting](https://github.com/researchmm/AOT-GAN-for-Inpainting), cited as
```
@inproceedings{yan2021agg,
author = {Zeng, Yanhong and Fu, Jianlong and Chao, Hongyang and Guo, Baining},
title = {Aggregated Contextual Transformations for High-Resolution Image Inpainting},
booktitle = {Arxiv},
pages={-},
year = {2020}
}
```
## Dataset
The Places2 dataset can be found here: http://places2.csail.mit.edu/download.html | {"tags": ["scene-recognition", "scene-generation", "generative-adversarial-network"], "datasets": ["places2"], "metrics": ["L1", "PSNR", "SSIM", "FID"]} | NimaBoscarino/aot-gan-places2 | null | [
"transformers",
"pytorch",
"scene-recognition",
"scene-generation",
"generative-adversarial-network",
"dataset:places2",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | NimaFar/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers | {} | NinaR21/Albert_funny | null | [
"transformers",
"tf",
"albert",
"feature-extraction",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Ninja5000/DialoGPT-medium-HarryPotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# DialoGPT-medium-TWEWYJoshua
Another not-so-good AI chatbot. Joshua from the game TWEWY(The World Ends With You).
* Credits to Lynn's Devlab who made the amazing tutorial. | {"tags": ["conversational"]} | Ninja5000/DialoGPT-medium-TWEWYJoshua | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
#LOTR DialoGPT Model | {"tags": ["conversational"]} | Niphredil/DialoGPT-small-lotr | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Nirmal/nlp_v1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | license: apache-2.0
---
### Rick DialoGPT Model | {"tags": ["conversational"]} | Nisarg2701/DialoGPT-medium-Rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Nive/xls-r-en-t1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nivedhan/WebOrders | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Nix/model-1 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | # ELECTRA
## Introduction
**ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
Electra-base-vn is trained on more 148gb text with max length 512.
You can download tensorflow version at [Electra base TF version](https://drive.google.com/drive/folders/1hN0LiOlMfNDDQVo2bgEYHd03I-xXDLVr?usp=sharing)
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]). | {} | NlpHUST/electra-base-vn | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | transformers | {} | NlpHUST/electra-legal-vi | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# GPT-Neo-small for vietnamese
First GPT for vietnamese
## Model Description
GPT-Neo-vi-small is a transformer model designed using EleutherAI's replication of the GPT-3 architecture.
## Training data
GPT-Neo-vi-smal was trained on the News datasets, a large scale dataset created by from News Website for the purpose of training this model.
### How to use
his example generates a different sequence each time it's run:
```py
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
model = GPTNeoForCausalLM.from_pretrained("NlpHUST/gpt-neo-vi-small")
tokenizer = GPT2Tokenizer.from_pretrained("NlpHUST/gpt-neo-vi-small")
prompt = "Ngay sau Tết Nguyên đán Tân Sửu, hiện tượng giá đất tăng tại nhiều địa phương. Thị trường nhộn nhịp, tạo ra những cơn sóng sốt đất khó tin khiến bộ ngành, địa phương đưa cảnh báo."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
gen_tokens = model.generate(input_ids, do_sample=True, temperature=1.0, max_length=1024)
gen_text = tokenizer.batch_decode(gen_tokens)[0]
print(gen_text)
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]). | {"language": "vi", "tags": ["vi", "vietnamese", "text-generation", "gpt3", "lm", "nlp"], "datasets": ["vietnamese"], "widget": [{"text": "Vi\u1ec7t Nam l\u00e0 qu\u1ed1c gia c\u00f3"}], "pipeline_tag": "text-generation"} | NlpHUST/gpt-neo-vi-small | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"vi",
"vietnamese",
"gpt3",
"lm",
"nlp",
"dataset:vietnamese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | # T5-EN-VI-BASE:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
# Dataset
The *IWSLT'15 English-Vietnamese* data is used from [Stanford NLP group](https://nlp.stanford.edu/projects/nmt/).
For all experiments the corpus was split into training, development and test set:
| Data set | Sentences | Download
| ----------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------
| Training | 133,317 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/train-en-vi.tgz) or located in `data/train-en-vi.tgz`
| Development | 1,553 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/dev-2012-en-vi.tgz) or located in `data/dev-2012-en-vi.tgz`
| Test | 1,268 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/test-2013-en-vi.tgz) or located in `data/test-2013-en-vi.tgz`
## Results
The results on test set.
| Model | BLEU (Beam Search)
| ----------------------------------------------------------------------------------------------------- | ------------------
| [Luong & Manning (2015)](https://nlp.stanford.edu/pubs/luong-manning-iwslt15.pdf) | 23.30
| Sequence-to-sequence model with attention | 26.10
| Neural Phrase-based Machine Translation [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 27.69
| Neural Phrase-based Machine Translation + LM [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 28.07
| t5-en-vi-small (pretraining, without training data) | **28.46** (cased) / **29.23** (uncased)
|t5-en-vi-small (fineturning with training data) | **32.38** (cased) / **33.19** (uncased)
| t5-en-vi-base (pretraining, without training data) | **29.66** (cased) / **30.37** (uncased)
#### Example Using
``` bash
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
model = T5ForConditionalGeneration.from_pretrained("NlpHUST/t5-en-vi-small")
tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-en-vi-small")
model.to(device)
src = "In school , we spent a lot of time studying the history of Kim Il-Sung , but we never learned much about the outside world , except that America , South Korea , Japan are the enemies ."
tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device)
model.eval()
summary_ids = model.generate(
tokenized_text,
max_length=128,
num_beams=5,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
```
#### Output
``` bash
Ở trường, chúng tôi dành nhiều thời gian để nghiên cứu về lịch sử Kim Il-Sung, nhưng chúng tôi chưa bao giờ học được nhiều về thế giới bên ngoài, ngoại trừ Mỹ, Hàn Quốc, Nhật Bản là kẻ thù.
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]). | {} | NlpHUST/t5-en-vi-base | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:1706.05565",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | # T5-EN-VI-SMALL:Pretraining Text-To-Text Transfer Transformer for English Vietnamese Translation
# Dataset
The *IWSLT'15 English-Vietnamese* data is used from [Stanford NLP group](https://nlp.stanford.edu/projects/nmt/).
For all experiments the corpus was split into training, development and test set:
| Data set | Sentences | Download
| ----------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------
| Training | 133,317 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/train-en-vi.tgz) or located in `data/train-en-vi.tgz`
| Development | 1,553 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/dev-2012-en-vi.tgz) or located in `data/dev-2012-en-vi.tgz`
| Test | 1,268 | via [GitHub](https://github.com/stefan-it/nmt-en-vi/raw/master/data/test-2013-en-vi.tgz) or located in `data/test-2013-en-vi.tgz`
## Results
The results on test set.
| Model | BLEU (Beam Search)
| ----------------------------------------------------------------------------------------------------- | ------------------
| [Luong & Manning (2015)](https://nlp.stanford.edu/pubs/luong-manning-iwslt15.pdf) | 23.30
| Sequence-to-sequence model with attention | 26.10
| Neural Phrase-based Machine Translation [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 27.69
| Neural Phrase-based Machine Translation + LM [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 28.07
| t5-en-vi-small (pretraining, without training data) | **28.46** (cased) / **29.23** (uncased)
|t5-en-vi-small (fineturning with training data) | **32.38** (cased) / **33.19** (uncased)
#### Example Using
``` bash
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
model = T5ForConditionalGeneration.from_pretrained("NlpHUST/t5-en-vi-small")
tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-en-vi-small")
model.to(device)
src = "In school , we spent a lot of time studying the history of Kim Il-Sung , but we never learned much about the outside world , except that America , South Korea , Japan are the enemies ."
tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device)
model.eval()
summary_ids = model.generate(
tokenized_text,
max_length=128,
num_beams=5,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
```
#### Output
``` bash
Ở trường, chúng tôi dành nhiều thời gian để nghiên cứu về lịch sử Kim Il-Sung, nhưng chúng tôi chưa bao giờ học được nhiều về thế giới bên ngoài, ngoại trừ Mỹ, Hàn Quốc, Nhật Bản là kẻ thù.
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]). | {} | NlpHUST/t5-en-vi-small | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:1706.05565",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | # T5-SMALL-SUMMARIZATION :Pretraining Text-To-Text Transfer Transformer for Vietnamese Text Summarization
#### Example Using
``` bash
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
model = T5ForConditionalGeneration.from_pretrained("NlpHUST/t5-small-vi-summarization")
tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-small-vi-summarization")
model.to(device)
src = "Theo BHXH Việt Nam, nhiều doanh nghiệp vẫn chỉ đóng BHXH cho người lao động theo mức lương. \\\\
Dù quy định từ 1/1/2018, tiền lương tháng đóng BHXH gồm mức lương và thêm khoản bổ sung khác. \\\\
BHXH Việt Nam vừa có báo cáo về tình hình thực hiện chính sách BHXH thời gian qua. \\\\
Theo đó, tình trạng nợ, trốn đóng BHXH, BHTN vẫn xảy ra ở hầu hết các tỉnh, thành. \\\\
Thống kê tới ngày 31/12/2020, tổng số nợ BHXH, BHYT, BHTN là hơn 13.500 tỷ đồng, \\\\
chiếm 3,35 % số phải thu, trong đó: Số nợ BHXH bắt buộc là hơn 8.600 tỷ đồng, \\\\
nợ BHTN là 335 tỷ đồng. Liên quan tới tiền lương đóng BHXH, báo cáo của \\\\
BHXH Việt Nam cho thấy: Nhiều doanh nghiệp vẫn chủ yếu xây dựng thang, \\\\
bảng lương để đóng BHXH bằng mức thấp nhất. Tức là bằng mức lương tối \\\\
thiểu vùng, cộng thêm 7 % đối với lao động đã qua đào tạo nghề và cộng \\\\
thêm 5 % hoặc 7 % đối với lao động làm nghề hoặc công việc nặng nhọc, \\\\
độc hại, nguy hiểm, đặc biệt nặng nhọc độc hại và nguy hiểm. Đối với \\\\
lao động giữ chức vụ, khoảng 80 % doanh nghiệp đã xây dựng thang, \\\\
bảng lương cụ thể theo chức danh. Đơn cử như với chức vụ giám đốc \\\\
sản xuất, giám đốc điều hành, trưởng phòng. Còn lại các doanh nghiệp \\\\
xây dựng đối với lao động giữ chức vụ theo thang lương, bảng lương \\\\
chuyên môn nghiệp vụ và bảng phụ cấp chức vụ, phụ cấp trách nhiệm. \\\\
Thống kê của BHXH Việt Nam cũng cho thấy, đa số doanh nghiệp đã đăng \\\\
ký đóng BHXH cho người lao động theo mức lương mà không có khoản bổ \\\\
sung khác. Mặc dù quy định từ ngày 1/1/2018, tiền lương tháng đóng BHXH \\\\
gồm mức lương và thêm khoản bổ sung khác."
tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device)
model.eval()
summary_ids = model.generate(
tokenized_text,
max_length=256,
num_beams=5,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
```
#### Output
``` bash
Nhiều doanh nghiệp vẫn chủ yếu xây dựng thang, bảng lương để đóng BHXH bằng mức thấp nhất. \\
Dù quy định từ 1/1/2018, tiền lương tháng đóng BHXH gồm mức lương và thêm khoản bổ sung khác. \\
Thống kê của BHXH Việt Nam cho thấy, nhiều doanh nghiệp vẫn chỉ đóng BHXH \\
cho người lao động theo mức lương mà không có khoản bổ sung khác.
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]). | {} | NlpHUST/t5-small-vi-summarization | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-base is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-base was trained on 4M sentence pairs (english,vietnamese)
### How to use
```py
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
model = T5ForConditionalGeneration.from_pretrained("NlpHUST/t5-vi-en-base")
tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-vi-en-base")
model.to(device)
src = "Theo lãnh đạo Sở Y tế, 3 người này không có triệu chứng sốt, ho, khó thở, đã được lấy mẫu xét nghiệm và cách ly tập trung."
tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device)
model.eval()
summary_ids = model.generate(
tokenized_text,
max_length=256,
num_beams=5,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
According to the head of the Department of Health, the three people had no symptoms of fever, cough, shortness of breath, were taken samples for testing and concentrated quarantine.
``` | {} | NlpHUST/t5-vi-en-base | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text2text-generation | transformers | ---
language:
- vi
tags:
- t5
- seq2seq
# Machine translation for vietnamese
## Model Description
T5-vi-en-small is a transformer model for vietnamese machine translation designed using T5 architecture.
## Training data
T5-vi-en-small was trained on 4M sentence pairs (english,vietnamese)
### How to use
```py
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
model = T5ForConditionalGeneration.from_pretrained("NlpHUST/t5-vi-en-small")
tokenizer = T5Tokenizer.from_pretrained("NlpHUST/t5-vi-en-small")
model.to(device)
src = "Indonesia phỏng đoán nguyên nhân tàu ngầm chở 53 người mất tích bí ẩn"
tokenized_text = tokenizer.encode(src, return_tensors="pt").to(device)
model.eval()
summary_ids = model.generate(
tokenized_text,
max_length=256,
num_beams=5,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
Indonesia anticipates the cause of the submarine transporting 53 mysterious missing persons
``` | {} | NlpHUST/t5-vi-en-small | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | transformers | {} | NlpHUST/vi-electra-small | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | # BERT for Vietnamese is trained on more 20 GB news dataset
Apply for task sentiment analysis on using [AIViVN's comments dataset](https://www.aivivn.com/contests/6)
The model achieved 0.90268 on the public leaderboard, (winner's score is 0.90087)
Bert4news is used for a toolkit Vietnames(segmentation and Named Entity Recognition) at ViNLPtoolkit(https://github.com/bino282/ViNLP)
We use word sentencepiece, use basic bert tokenization and same config with bert base with lowercase = False.
You can download trained model:
- [tensorflow](https://drive.google.com/file/d/1X-sRDYf7moS_h61J3L79NkMVGHP-P-k5/view?usp=sharing).
- [pytorch](https://drive.google.com/file/d/11aFSTpYIurn-oI2XpAmcCTccB_AonMOu/view?usp=sharing).
Use with huggingface/transformers
``` bash
import torch
from transformers import BertTokenizer,BertModel
tokenizer= BertTokenizer.from_pretrained("NlpHUST/vibert4news-base-cased")
bert_model = BertModel.from_pretrained("NlpHUST/vibert4news-base-cased")
line = "Tôi là sinh viên trường Bách Khoa Hà Nội ."
input_id = tokenizer.encode(line,add_special_tokens = True)
att_mask = [int(token_id > 0) for token_id in input_id]
input_ids = torch.tensor([input_id])
att_masks = torch.tensor([att_mask])
with torch.no_grad():
features = bert_model(input_ids,att_masks)
print(features)
```
# Vietnamese toolkit with bert
ViNLP is a system annotation for Vietnamese, it use pretrain [Bert4news](https://github.com/bino282/bert4news/) to fine-turning to NLP problems in Vietnamese components of wordsegmentation,Named entity recognition (NER) and achieve high accuravy.
### Installation
```bash
git clone https://github.com/bino282/ViNLP.git
cd ViNLP
python setup.py develop build
```
### Test Segmentation
The model achieved F1 score : 0.984 on VLSP 2013 dataset
|Model | F1 |
|--------|-----------|
| **BertVnTokenizer** | 98.40 |
| **DongDu** | 96.90 |
| **JvnSegmenter-Maxent** | 97.00 |
| **JvnSegmenter-CRFs** | 97.06 |
| **VnTokenizer** | 97.33 |
| **UETSegmenter** | 97.87 |
| **VnTokenizer** | 97.33 |
| **VnCoreNLP (i.e. RDRsegmenter)** | 97.90 |
``` bash
from ViNLP import BertVnTokenizer
tokenizer = BertVnTokenizer()
sentences = tokenizer.split(["Tổng thống Donald Trump ký sắc lệnh cấm mọi giao dịch của Mỹ với ByteDance và Tecent - chủ sở hữu của 2 ứng dụng phổ biến TikTok và WeChat sau 45 ngày nữa."])
print(sentences[0])
```
``` bash
Tổng_thống Donald_Trump ký sắc_lệnh cấm mọi giao_dịch của Mỹ với ByteDance và Tecent - chủ_sở_hữu của 2 ứng_dụng phổ_biến TikTok và WeChat sau 45 ngày nữa .
```
### Test Named Entity Recognition
The model achieved F1 score VLSP 2018 for all named entities including nested entities : 0.786
|Model | F1 |
|--------|-----------|
| **BertVnNer** | 78.60 |
| **VNER Attentive Neural Network** | 77.52 |
| **vietner CRF (ngrams + word shapes + cluster + w2v)** | 76.63 |
| **ZA-NER BiLSTM** | 74.70 |
``` bash
from ViNLP import BertVnNer
bert_ner_model = BertVnNer()
sentence = "Theo SCMP, báo cáo của CSIS với tên gọi Định hình Tương lai Chính sách của Mỹ với Trung Quốc cũng cho thấy sự ủng hộ tương đối rộng rãi của các chuyên gia về việc cấm Huawei, tập đoàn viễn thông khổng lồ của Trung Quốc"
entities = bert_ner_model.annotate([sentence])
print(entities)
```
``` bash
[{'ORGANIZATION': ['SCMP', 'CSIS', 'Huawei'], 'LOCATION': ['Mỹ', 'Trung Quốc']}]
```
Run training with base config
``` bash
python train_pytorch.py \\\\
--model_path=bert4news.pytorch \\\\
--max_len=200 \\\\
--batch_size=16 \\\\
--epochs=6 \\\\
--lr=2e-5
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).
| {"language": "vn"} | NlpHUST/vibert4news-base-cased | null | [
"transformers",
"pytorch",
"safetensors",
"fill-mask",
"vn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Nlpxyz/firstnlp | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Hagrid DialoGPT medium model | {"tags": ["conversational"]} | NoLawz/DialoGPT-medium-hagrid | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Harry Potter DialoGPT medium model | {"tags": ["conversational"]} | NoLawz/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Spong Bob DialoGPT medium model | {"tags": ["conversational"]} | NoLawz/DialoGPT-medium-spongebob | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Noah23/jhgyfevhudkmls | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# NLGP docstring model
The NLGP docstring model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198). The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **context** (see the example below).
Also see the [NLGP natural](https://huggingface.co/Nokia/nlgp-natural) model.
This work was carried out by a research team in Nokia Bell Labs.
**Context**
```py
import matplotlib.pyplot as plt
values = [1, 2, 3, 4]
labels = ["a", "b", "c", "d"]
```
**Intent**
```py
# plot a bart chart
```
**Prediction**
```py
plt.bar(labels, values)
plt.show()
```
## Usage
```py
import re
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
# load the model
tok = GPT2TokenizerFast.from_pretrained("Nokia/nlgp-docstring")
model = GPT2LMHeadModel.from_pretrained("Nokia/nlgp-docstring")
# preprocessing functions
num_spaces = [2, 4, 6, 8, 10, 12, 14, 16, 18]
def preprocess(context, query):
"""
Encodes context + query as a single string and
replaces whitespace with special tokens <|2space|>, <|4space|>, ...
"""
input_str = f"{context}\n{query} <|endofcomment|>\n"
indentation_symbols = {n: f"<|{n}space|>" for n in num_spaces}
m = re.match("^[ ]+", input_str)
if not m:
return input_str
leading_whitespace = m.group(0)
N = len(leading_whitespace)
for n in self.num_spaces:
leading_whitespace = leading_whitespace.replace(n * " ", self.indentation_symbols[n])
return leading_whitespace + input_str[N:]
detokenize_pattern = re.compile(fr"<\|(\d+)space\|>")
def postprocess(output):
output = output.split("<|cell|>")[0]
def insert_space(m):
num_spaces = int(m.group(1))
return num_spaces * " "
return detokenize_pattern.sub(insert_space, output)
# inference
code_context = """
import matplotlib.pyplot as plt
values = [1, 2, 3, 4]
labels = ["a", "b", "c", "d"]
"""
query = "# plot a bar chart"
input_str = preprocess(code_context, query)
input_ids = tok(input_str, return_tensors="pt").input_ids
max_length = 150 # don't generate output longer than this length
total_max_length = min(1024 - input_ids.shape[-1], input_ids.shape[-1] + 150) # total = input + output
input_and_output = model.generate(
input_ids=input_ids,
max_length=total_max_length,
min_length=10,
do_sample=False,
num_beams=4,
early_stopping=True,
eos_token_id=tok.encode("<|cell|>")[0]
)
output = input_and_output[:, input_ids.shape[-1]:] # remove the tokens that correspond to the input_str
output_str = tok.decode(output[0])
postprocess(output_str)
```
## License and copyright
Copyright 2021 Nokia
Licensed under the Apache License 2.0
SPDX-License-Identifier: Apache-2.0 | {"language": ["en", "code"], "license": "apache-2.0", "tags": ["code completion", "code generation"]} | Nokia/nlgp-docstring | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"code completion",
"code generation",
"en",
"code",
"arxiv:2108.05198",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# NLGP natural model
The NLGP natural model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198). The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **context** (see the example below). This work was carried out by a research team in Nokia Bell Labs.
**Context**
```py
import matplotlib.pyplot as plt
values = [1, 2, 3, 4]
labels = ["a", "b", "c", "d"]
```
**Intent**
```py
# plot a bar chart
```
**Prediction**
```py
plt.bar(labels, values)
plt.show()
```
## Usage
```py
import re
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
# load the model
tok = GPT2TokenizerFast.from_pretrained("Nokia/nlgp-natural")
model = GPT2LMHeadModel.from_pretrained("Nokia/nlgp-natural")
# preprocessing functions
num_spaces = [2, 4, 6, 8, 10, 12, 14, 16, 18]
def preprocess(context, query):
"""
Encodes context + query as a single string and
replaces whitespace with special tokens <|2space|>, <|4space|>, ...
"""
input_str = f"{context}\n{query} <|endofcomment|>\n"
indentation_symbols = {n: f"<|{n}space|>" for n in num_spaces}
m = re.match("^[ ]+", input_str)
if not m:
return input_str
leading_whitespace = m.group(0)
N = len(leading_whitespace)
for n in self.num_spaces:
leading_whitespace = leading_whitespace.replace(n * " ", self.indentation_symbols[n])
return leading_whitespace + input_str[N:]
detokenize_pattern = re.compile(fr"<\|(\d+)space\|>")
def postprocess(output):
output = output.split("<|cell|>")[0]
def insert_space(m):
num_spaces = int(m.group(1))
return num_spaces * " "
return detokenize_pattern.sub(insert_space, output)
# inference
code_context = """
import matplotlib.pyplot as plt
values = [1, 2, 3, 4]
labels = ["a", "b", "c", "d"]
"""
query = "# plot a bar chart"
input_str = preprocess(code_context, query)
input_ids = tok(input_str, return_tensors="pt").input_ids
max_length = 150 # don't generate output longer than this length
total_max_length = min(1024 - input_ids.shape[-1], input_ids.shape[-1] + 150) # total = input + output
input_and_output = model.generate(
input_ids=input_ids,
max_length=total_max_length,
min_length=10,
do_sample=False,
num_beams=4,
early_stopping=True,
eos_token_id=tok.encode("<|cell|>")[0]
)
output = input_and_output[:, input_ids.shape[-1]:] # remove the tokens that correspond to the input_str
output_str = tok.decode(output[0])
postprocess(output_str)
```
## License and copyright
Copyright 2021 Nokia
Licensed under the Apache License 2.0
SPDX-License-Identifier: Apache-2.0 | {"language": ["en", "code"], "license": "apache-2.0", "tags": ["code completion", "code generation"]} | Nokia/nlgp-natural | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"code completion",
"code generation",
"en",
"code",
"arxiv:2108.05198",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Noman/layoutlmv2 | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Noman/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers | {} | Nomi97/Chatbot_QA | null | [
"transformers",
"pytorch",
"longformer",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Noobanand69420/Octane | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | NoodleOnHuggingFace/AITestModel-small-joshua | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Noodlezs/Monica | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Noremac/b | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | NoriZ/semval-finetune | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | # Wav2vec2 German Model
This model has been fine-tuned on the wav2vec-large-xlsr-53 with the German CommonVoice dataset.
It achieves a 11.26 WER on the full test dataset.
It was basically trained with the code provided by [Max Idahl](https://huggingface.co/maxidl/wav2vec2-large-xlsr-german) with small adjustments in data preprocessing and on training parameters.
You can use it to transcribe your own files by the following code. Please note, that your input file must be *.wav, encoded in 16 kHz and be single channel. To convert an audio file using ffmpeg use: "ffmpeg -i input.wav -ar 16000 -ac 1 output.wav". The transcribe process is very memory consuming (around 10GB per 10 seconds). If the script ends with "Killed" it means the Python interpreter ran out of memory. In this case, try with a shorter audio file.
```python
# !pip3 install transformers torch soundfile
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
# load pretrained model
tokenizer = Wav2Vec2Tokenizer.from_pretrained("Noricum/wav2vec2-large-xlsr-53-german")
model = Wav2Vec2ForCTC.from_pretrained("Noricum/wav2vec2-large-xlsr-53-german")
#load audio
audio_input, _ = sf.read("/path/to/your/audio.wav")
# transcribe
input_values = tokenizer(audio_input, return_tensors="pt").input_values
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = tokenizer.batch_decode(predicted_ids)[0]
print(str(transcription))
```
To evaluate the model on the full CommonVoice test dataset, run this script:
```python
import re
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "de", split="test") # use "test[:1%]" for 1% sample
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("Noricum/wav2vec2-large-xlsr-53-german")
model = Wav2Vec2ForCTC.from_pretrained("Noricum/wav2vec2-large-xlsr-53-german")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=4) # batch_size=8 -> requires ~14.5GB GPU memory
# Chunked version, see https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5:
import jiwer
def chunked_wer(targets, predictions, chunk_size=None):
if chunk_size is None: return jiwer.wer(targets, predictions)
start = 0
end = chunk_size
H, S, D, I = 0, 0, 0, 0
while start < len(targets):
chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
H = H + chunk_metrics["hits"]
S = S + chunk_metrics["substitutions"]
D = D + chunk_metrics["deletions"]
I = I + chunk_metrics["insertions"]
start += chunk_size
end += chunk_size
return float(S + D + I) / float(H + S + D)
print("Total (chunk_size=1000), WER: {:2f}".format(100 * chunked_wer(result["pred_strings"], result["sentence"], chunk_size=1000)))
```
Output: Total (chunk_size=1000), WER: 11.256522
| {} | Noricum/wav2vec2-large-xlsr-53-german | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers | {} | Norimoji/DialoGPT-medium-FF7 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# distilgpt2-base-pretrained-he
A tiny GPT2 based Hebrew text generation model initially trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. Then was further fine-tuned on GPU.
## Dataset
### oscar (unshuffled deduplicated he) - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
### CC-100 (he) - [HomePage](https://data.statmt.org/cc-100/)
This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.
### Misc
* Hebrew Twitter
* Wikipedia
* Various other sources
## Training
* Done on a TPUv3-8 VM using [Huggingface's clm-flax example script](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_clm_flax.py) <BR>
* I have made a list of items which might make it easier for other to use this script. The list was posted to [This discussion forum](https://discuss.huggingface.co/t/ideas-for-beginner-friendlier-tpu-vm-clm-training/8351)
* Further training was performed on GPU
## Usage
#### Simple usage sample code
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
def main():
model_name="Norod78/distilgpt2-base-pretrained-he"
prompt_text = "שלום, קוראים לי"
generated_max_length = 192
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(model_name)
print('Loading Tokenizer...')
tokenizer = AutoTokenizer.from_pretrained(model_name)
text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
print("Generating text...")
result = text_generator(prompt_text, num_return_sequences=1, batch_size=1, do_sample=True, top_k=40, top_p=0.92, temperature = 1, repetition_penalty=5.0, max_length = generated_max_length)
print("result = " + str(result))
if __name__ == '__main__':
main()
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05d4\u05d0\u05d9\u05e9 \u05d4\u05d0\u05d7\u05e8\u05d5\u05df \u05e2\u05dc\u05d9 \u05d0\u05d3\u05de\u05d5\u05ea \u05d9\u05e9\u05d1 \u05dc\u05d1\u05d3 \u05d1\u05d7\u05d3\u05e8\u05d5 \u05db\u05e9\u05dc\u05e4\u05ea\u05e2 \u05e0\u05e9\u05de\u05e2\u05d4 \u05e0\u05e7\u05d9\u05e9\u05d4"}, {"text": "\u05e9\u05dc\u05d5\u05dd, \u05e7\u05e8\u05d5\u05d0\u05d9\u05dd \u05dc\u05d9"}, {"text": "\u05d4\u05d0\u05e8\u05d9 \u05e4\u05d5\u05d8\u05e8 \u05d7\u05d9\u05d9\u05da \u05d7\u05d9\u05d5\u05da \u05e0\u05d1\u05d5\u05da"}, {"text": "\u05d4\u05d7\u05ea\u05d5\u05dc \u05e9\u05dc\u05da \u05de\u05d0\u05d5\u05d3 \u05d7\u05de\u05d5\u05d3 \u05d5"}]} | Norod78/distilgpt2-base-pretrained-he | null | [
"transformers",
"pytorch",
"tf",
"jax",
"coreml",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers | {} | Norod78/english-sienfeld-distilgpt2 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# hebrew-bad_wiki-gpt_neo-tiny
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
**Model Description:**
The model developer notes that the model is
> Hebrew nonsense generation model which produces really bad wiki-abstract text.
- **Developed by:** [Doron Adler](https://github.com/Norod)
- **Model Type:** Text Generation
- **Language(s):** Hebrew
- **License:** MIT
- **Resources for more information:**
- [GitHub Repo](https://github.com/Norod/hebrew-gpt_neo)
- [HuggingFace Space](https://huggingface.co/spaces/Norod78/Hebrew-GPT-Neo-Small)
## Uses
#### Direct Use
This model can be used for text generation.
#### Misuse and Out-of-scope Use
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## Training
#### Training Data
[Hebrew Wikipedia Dump](https://dumps.wikimedia.org/hewiki/latest/) (hewiki abstract) from May 2020
#### Training Procedure
This model was fined tuned upon [hebrew-gpt_neo-tiny](https://huggingface.co/Norod78/hebrew-gpt_neo-tiny) which was previously trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo).
Fine-tuning on the wiki-absract text was done using [@minimaxir](https://twitter.com/minimaxir)'s [aitextgen](https://github.com/minimaxir/aitextgen).
## Evaluation
#### Configs
Model configs for the hebrew-gpt_neo-tiny is available on the [hebrew-gpt_neo model github](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-tiny/configs)
* **Activation Function:** gelu
* **Number_Head:** 12
* **Number_Vocab:** 50257
* **Train batch size:** 250
* **Eval batch size:** 64
* **Predict batch size:** 1
## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).
- **Hardware Type:** [More information needed]
- **Hours used:** Unknown
- **Cloud Provider:** GCP tpu-v8s
- **Compute Region:** europe-west4
- **Carbon Emitted:** [More information needed]
## How to Get Started With the Model
A Google Colab Notebook is also available [here](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-tiny/Norod78_hebrew_gpt_neo_tiny_Colab.ipynb)
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05de\u05ea\u05de\u05d8\u05d9\u05e7\u05d4:"}, {"text": "\u05e2\u05dc\u05d9\u05d9\u05ea \u05d4\u05de\u05db\u05d5\u05e0\u05d5\u05ea"}, {"text": "\u05d5\u05d9\u05e7\u05d9\u05e4\u05d3\u05d9\u05d4 \u05d4\u05e2\u05d1\u05e8\u05d9\u05ea"}, {"text": "\u05d4\u05d0\u05d9\u05e8\u05d5\u05d5\u05d9\u05d6\u05d9\u05d5\u05df \u05d4\u05d5\u05d0"}, {"text": "\u05d3\u05d5\u05d3 \u05d1\u05df-\u05d2\u05d5\u05e8\u05d9\u05d5\u05df \u05d4\u05d9\u05d4"}]} | Norod78/hebrew-bad_wiki-gpt_neo-tiny | null | [
"transformers",
"pytorch",
"coreml",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"arxiv:1910.09700",
"arxiv:2105.09680",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hebrew-gpt_neo-small
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ)
2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
3. CC100-Hebrew Dataset [Homepage](https://metatext.io/datasets/cc100-hebrew)
Created by Conneau & Wenzek et al. at 2020, the CC100-Hebrew This dataset is one of the 100 corpora of monolingual data that was processed from the January-December 2018 Commoncrawl snapshots from the CC-Net repository. The size of this corpus is 6.1G., in Hebrew language.
## Training Config
Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-small/configs) <BR>
## Usage
### Google Colab Notebook
Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-small/Norod78_hebrew_gpt_neo_small_Colab.ipynb) <BR>
#### Simple usage sample code
```python
!pip install tokenizers==0.10.2 transformers==4.6.0
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-small")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-small", pad_token_id=tokenizer.eos_token_id)
prompt_text = "אני אוהב שוקולד ועוגות"
max_len = 512
sample_output_num = 3
seed = 1000
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
print(f"device: {device}, n_gpu: {n_gpu}")
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
model.to(device)
encoded_prompt = tokenizer.encode(
prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
print("input_ids = " + str(input_ids))
if input_ids != None:
max_len += len(encoded_prompt[0])
if max_len > 2048:
max_len = 2048
print("Updated max_len = " + str(max_len))
stop_token = "<|endoftext|>"
new_lines = "\n\n\n"
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=max_len,
top_k=50,
top_p=0.95,
num_return_sequences=sample_output_num
)
print(100 * '-' + "\n\t\tOutput\n" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
text = tokenizer.decode(sample_output, skip_special_tokens=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Remove all text after 3 newlines
text = text[: text.find(new_lines) if new_lines else None]
print("\n{}: {}".format(i, text))
print("\n" + 100 * '-')
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d3\u05d5\u05e8\u05d5\u05df \u05d5\u05d0\u05e0\u05d9 \u05de\u05e2\u05d5\u05e0\u05d9\u05d9\u05df \u05dc"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d0\u05d9\u05e6\u05d9\u05e7 \u05d5\u05d0\u05e0\u05d9 \u05d7\u05d5\u05e9\u05d1 \u05e9"}, {"text": "\u05d4\u05d7\u05ea\u05d5\u05dc \u05e9\u05dc\u05da \u05de\u05d0\u05d5\u05d3 \u05d7\u05de\u05d5\u05d3 \u05d5"}]} | Norod78/hebrew-gpt_neo-small | null | [
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hebrew-gpt_neo-tiny
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ)
2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
## Training Config
Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-tiny/configs) <BR>
## Usage
### Google Colab Notebook
Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-tiny/Norod78_hebrew_gpt_neo_tiny_Colab.ipynb) <BR>
#### Simple usage sample code
```python
!pip install tokenizers==0.10.2 transformers==4.6.0
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-tiny")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-tiny", pad_token_id=tokenizer.eos_token_id)
prompt_text = "אני אוהב שוקולד ועוגות"
max_len = 512
sample_output_num = 3
seed = 1000
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
print(f"device: {device}, n_gpu: {n_gpu}")
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
model.to(device)
encoded_prompt = tokenizer.encode(
prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
print("input_ids = " + str(input_ids))
if input_ids != None:
max_len += len(encoded_prompt[0])
if max_len > 1024:
max_len = 1024
print("Updated max_len = " + str(max_len))
stop_token = "<|endoftext|>"
new_lines = "\n\n\n"
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=max_len,
top_k=50,
top_p=0.95,
num_return_sequences=sample_output_num
)
print(100 * '-' + "\n\t\tOutput\n" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
text = tokenizer.decode(sample_output, skip_special_tokens=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Remove all text after 3 newlines
text = text[: text.find(new_lines) if new_lines else None]
print("\n{}: {}".format(i, text))
print("\n" + 100 * '-')
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d3\u05d5\u05e8\u05d5\u05df \u05d5\u05d0\u05e0\u05d9 \u05de\u05e2\u05d5\u05e0\u05d9\u05d9\u05df \u05dc"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d0\u05d9\u05e6\u05d9\u05e7 \u05d5\u05d0\u05e0\u05d9 \u05d7\u05d5\u05e9\u05d1 \u05e9"}, {"text": "\u05d4\u05d7\u05ea\u05d5\u05dc \u05e9\u05dc\u05da \u05de\u05d0\u05d5\u05d3 \u05d7\u05de\u05d5\u05d3 \u05d5"}]} | Norod78/hebrew-gpt_neo-tiny | null | [
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hebrew-gpt_neo-xl-poetry
Hebrew poetry text generation model which was fine tuned upon on [hebrew-gpt_neo-xl](https://huggingface.co/Norod78/hebrew-gpt_neo-xl).
## Datasets
An assortment of various Hebrew books, magazines and poetry corpuses
## Training Config
Similar to [this one](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-xl/configs) <BR>
## Usage
### Google Colab Notebook
Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-xl/Norod78_hebrew_gpt_neo_xl_Colab.ipynb) <BR>
#### Simple usage sample code
```python
!pip install tokenizers==0.10.3 transformers==4.8.0
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl-poetry")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl-poetry", pad_token_id=tokenizer.eos_token_id)
prompt_text = "אני אוהב שוקולד ועוגות"
max_len = 512
sample_output_num = 3
seed = 1000
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
print(f"device: {device}, n_gpu: {n_gpu}")
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
model.to(device)
encoded_prompt = tokenizer.encode(
prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
print("input_ids = " + str(input_ids))
if input_ids != None:
max_len += len(encoded_prompt[0])
if max_len > 2048:
max_len = 2048
print("Updated max_len = " + str(max_len))
stop_token = "<|endoftext|>"
new_lines = "\n\n\n"
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=max_len,
top_k=50,
top_p=0.95,
num_return_sequences=sample_output_num
)
print(100 * '-' + "\n\t\tOutput\n" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
text = tokenizer.decode(sample_output, skip_special_tokens=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Remove all text after 3 newlines
text = text[: text.find(new_lines) if new_lines else None]
print("\n{}: {}".format(i, text))
print("\n" + 100 * '-')
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05ea\u05e8\u05d9\u05e1\u05e8 \u05de\u05db\u05e9\u05e4\u05d5\u05ea \u05e1\u05d2"}, {"text": "\n\n\u05d4\u05d0\u05d9\u05e9 \u05d4\u05d0\u05d7\u05e8\u05d5\u05df \u05d1\u05e2\u05d5\u05dc\u05dd /"}, {"text": "\u05e4\u05e2\u05dd \u05d0\u05d7\u05ea, \u05dc\u05e4\u05e0\u05d9 \u05e9\u05e0\u05d9\u05dd \u05e8\u05d1\u05d5\u05ea"}, {"text": "\u05d4\u05e8\u05de\u05d9\u05d5\u05e0\u05d9 \u05d4\u05e1\u05ea\u05d9\u05e8\u05d4 \u05d0\u05ea"}, {"text": "\u05dc\u05e4\u05ea\u05e2, \u05d0\u05d5\u05e8 \u05d9\u05e8\u05d5\u05e7"}]} | Norod78/hebrew-gpt_neo-xl-poetry | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hebrew-gpt_neo-xl
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available [here](https://mega.nz/folder/CodSSA4R#4INvMes-56m_WUi7jQMbJQ)
2. oscar / unshuffled_deduplicated_he - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
3. CC100-Hebrew Dataset [Homepage](https://metatext.io/datasets/cc100-hebrew)
Created by Conneau & Wenzek et al. at 2020, the CC100-Hebrew This dataset is one of the 100 corpora of monolingual data that was processed from the January-December 2018 Commoncrawl snapshots from the CC-Net repository. The size of this corpus is 6.1G., in Hebrew language.
## Training Config
Available [here](https://github.com/Norod/hebrew-gpt_neo/tree/main/hebrew-gpt_neo-xl/configs) <BR>
## Usage
### Google Colab Notebook
Available [here ](https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew-gpt_neo-xl/Norod78_hebrew_gpt_neo_xl_Colab.ipynb) <BR>
#### Simple usage sample code
```python
!pip install tokenizers==0.10.3 transformers==4.8.0
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl", pad_token_id=tokenizer.eos_token_id)
prompt_text = "אני אוהב שוקולד ועוגות"
max_len = 512
sample_output_num = 3
seed = 1000
import numpy as np
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
print(f"device: {device}, n_gpu: {n_gpu}")
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
model.to(device)
encoded_prompt = tokenizer.encode(
prompt_text, add_special_tokens=False, return_tensors="pt")
encoded_prompt = encoded_prompt.to(device)
if encoded_prompt.size()[-1] == 0:
input_ids = None
else:
input_ids = encoded_prompt
print("input_ids = " + str(input_ids))
if input_ids != None:
max_len += len(encoded_prompt[0])
if max_len > 2048:
max_len = 2048
print("Updated max_len = " + str(max_len))
stop_token = "<|endoftext|>"
new_lines = "\
\
\
"
sample_outputs = model.generate(
input_ids,
do_sample=True,
max_length=max_len,
top_k=50,
top_p=0.95,
num_return_sequences=sample_output_num
)
print(100 * '-' + "\
\t\tOutput\
" + 100 * '-')
for i, sample_output in enumerate(sample_outputs):
text = tokenizer.decode(sample_output, skip_special_tokens=True)
# Remove all text after the stop token
text = text[: text.find(stop_token) if stop_token else None]
# Remove all text after 3 newlines
text = text[: text.find(new_lines) if new_lines else None]
print("\
{}: {}".format(i, text))
print("\
" + 100 * '-')
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d3\u05d5\u05e8\u05d5\u05df \u05d5\u05d0\u05e0\u05d9 \u05de\u05e2\u05d5\u05e0\u05d9\u05d9\u05df \u05dc"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d0\u05d9\u05e6\u05d9\u05e7 \u05d5\u05d0\u05e0\u05d9 \u05d7\u05d5\u05e9\u05d1 \u05e9"}, {"text": "\u05d4\u05d7\u05ea\u05d5\u05dc \u05e9\u05dc\u05da \u05de\u05d0\u05d5\u05d3 \u05d7\u05de\u05d5\u05d3 \u05d5"}, {"text": "\u05d5\u05d1\u05d3\u05e8\u05da \u05e8\u05d0\u05d9\u05e0\u05d5 \u05e9\u05d4\u05d2\u05df"}]} | Norod78/hebrew-gpt_neo-xl | null | [
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hebrew_poetry-gpt_neo-small
Hebrew poetry text generation model, fined tuned upon [hebrew-gpt_neo-small](https://huggingface.co/Norod78/hebrew-gpt_neo-small) which was trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo).
Fine-tuning was done using [@minimaxir](https://twitter.com/minimaxir)'s [aitextgen](https://github.com/minimaxir/aitextgen).
## Datasets
1. Text from [New stage](http://stage.co.il/)
2. A dataset containing Hebrew lyrics
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e4\u05e2\u05dd \u05d0\u05d7\u05ea \u05dc\u05e4\u05e0\u05d9 \u05e9\u05e0"}, {"text": "\u05d4\u05d9\u05dd \u05db\u05d7\u05d5\u05dc \u05d5\u05d0\u05e0\u05d9 \u05d7"}, {"text": "\u05e9\u05dd \u05d4\u05d9\u05e6\u05d9\u05e8\u05d4:"}, {"text": "\u05db\u05e9\u05d4\u05de\u05db\u05d5\u05e0\u05d5\u05ea"}]} | Norod78/hebrew_poetry-gpt_neo-small | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hebrew_stories-gpt_neo-small
Hebrew story-text generation model, fined tuned upon [hebrew-gpt_neo-small](https://huggingface.co/Norod78/hebrew-gpt_neo-small) which was trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo).
## Dataset
Text from various Hebrew books
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05ea\u05e8\u05d9\u05e1\u05e8 \u05de\u05db\u05e9\u05e4\u05d5\u05ea \u05e1\u05d2"}, {"text": "\n\n\u05d4\u05d0\u05d9\u05e9 \u05d4\u05d0\u05d7\u05e8\u05d5\u05df \u05d1\u05e2\u05d5\u05dc\u05dd /"}, {"text": "\u05e4\u05e2\u05dd \u05d0\u05d7\u05ea, \u05dc\u05e4\u05e0\u05d9 \u05e9\u05e0\u05d9\u05dd \u05e8\u05d1\u05d5\u05ea"}, {"text": "\u05d4\u05e8\u05de\u05d9\u05d5\u05e0\u05d9 \u05d4\u05e1\u05ea\u05d9\u05e8\u05d4 \u05d0\u05ea"}, {"text": "\u05dc\u05e4\u05ea\u05e2, \u05d0\u05d5\u05e8 \u05d9\u05e8\u05d5\u05e7"}]} | Norod78/hebrew_stories-gpt_neo-small | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# hewiki-articles-distilGPT2py-il
## A tiny GPT2 model for generating Hebrew text
A distilGPT2 sized model. <br>
Training data was hewiki-20200701-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/hewiki/20200701/ <br>
XML has been converted to plain text using Wikipedia Extractor http://medialab.di.unipi.it/wiki/Wikipedia_Extractor <br>
I then added <|startoftext|> and <|endoftext|> markers and deleted empty lines. <br>
#### How to use
```python
import torch
import torch.nn as nn
from transformers import GPT2Tokenizer, GPT2LMHeadModel
tokenizer = GPT2Tokenizer.from_pretrained("Norod78/hewiki-articles-distilGPT2py-il")
model = GPT2LMHeadModel.from_pretrained("Norod78/hewiki-articles-distilGPT2py-il").eval()
bos_token = tokenizer.bos_token #Beginning of sentace
eos_token = tokenizer.eos_token #End of sentence
def generate_word(model, tokens_tensor, temperature=1.0):
"""
Sample a word given a tensor of tokens of previous words from a model. Given
the words we have, sample a plausible word. Temperature is used for
controlling randomness. If using temperature==0 we simply use a greedy arg max.
Else, we sample from a multinomial distribution using a lower inverse
temperature to allow for more randomness to escape repetitions.
"""
with torch.no_grad():
outputs = model(tokens_tensor)
predictions = outputs[0]
if temperature>0:
# Make the distribution more or less skewed based on the temperature
predictions = outputs[0]/temperature
# Sample from the distribution
softmax = nn.Softmax(dim=0)
predicted_index = torch.multinomial(softmax(predictions[0,-1,:]),1).item()
# Simply take the arg-max of the distribution
else:
predicted_index = torch.argmax(predictions[0, -1, :]).item()
# Decode the encoding to the corresponding word
predicted_text = tokenizer.decode([predicted_index])
return predicted_text
def generate_sentence(model, tokenizer, initial_text, temperature=1.0):
""" Generate a sentence given some initial text using a model and a tokenizer.
Returns the new sentence. """
# Encode a text inputs
text = ""
sentence = text
# We avoid an infinite loop by setting a maximum range
for i in range(0,84):
indexed_tokens = tokenizer.encode(initial_text + text)
# Convert indexed tokens in a PyTorch tensor
tokens_tensor = torch.tensor([indexed_tokens])
new_word = generate_word(model, tokens_tensor, temperature=temperature)
# Here the temperature is slowly decreased with each generated word,
# this ensures that the sentence (ending) makes more sense.
# We don't decrease to a temperature of 0.0 to leave some randomness in.
if temperature<(1-0.008):
temperature += 0.008
else:
temperature = 0.996
text = text+new_word
# Stop generating new words when we have reached the end of the line or the poem
if eos_token in new_word:
# returns new sentence and whether poem is done
return (text.replace(eos_token,"").strip(), True)
elif '/' in new_word:
return (text.strip(), False)
elif bos_token in new_word:
return (text.replace(bos_token,"").strip(), False)
return (text, True)
for output_num in range(1,5):
init_text = "בוקר טוב"
text = bos_token + init_text
for i in range(0,84):
sentence = generate_sentence(model, tokenizer, text, temperature=0.9)
text = init_text + sentence[0]
print(text)
if (sentence[1] == True):
break
```
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "<|startoftext|>\u05d4\u05d7\u05d5\u05e7 \u05d4\u05e9\u05e0\u05d9 \u05e9\u05dc \u05de\u05d5\u05e2\u05d3\u05d5\u05df \u05e7\u05e8\u05d1 \u05d4\u05d5\u05d0"}, {"text": "<|startoftext|>\u05e8\u05d0\u05e9 \u05d4\u05de\u05de\u05e9\u05dc\u05d4 \u05d1\u05df \u05d2\u05d5\u05e8\u05d9\u05d5\u05df"}, {"text": "<|startoftext|>\u05dc\u05de\u05d9\u05d3\u05ea \u05de\u05db\u05d5\u05e0\u05d4 (\u05e1\u05e8\u05d8)"}, {"text": "<|startoftext|>\u05de\u05e0\u05e9\u05d4 \u05e4\u05d5\u05de\u05e4\u05e8\u05e0\u05d9\u05e7\u05dc"}, {"text": "<|startoftext|>\u05d0\u05d9 \u05e9\u05d5\u05d5\u05d9\u05d5\u05df "}]} | Norod78/hewiki-articles-distilGPT2py-il | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"he",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers | {} | Norrawee/monsoon-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Norrawee/mosoon-ner | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | Norrawee/wangchanberta-ner-2 | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | Norrawee/wangchanberta-w10 | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | Norrawee/wangchanberta-w20 | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | Norrawee/wangchanberta-w50 | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Not/test-model | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | NotSage/sage | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | NotSage/sagecodes | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Noureddine/model_name | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Noureddine/xlnet-base | null | [
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
#Lelouch DialoGPT model | {"tags": ["conversational"]} | Nova/DialoGPT-medium-Lelouch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | NovaChrono/twervy | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Genji-JP 6B
Please check our blog post for more details, samples, evaluations and more:
[Blogpost](https://blog.novelai.net/data-efficient-language-transfer-with-gpt-j-45daedaaf35a)
## Model Description
Genji-JP 6B is a model finetuned on our Japanese storytelling dataset based on EleutherAI's GPT-J 6B model. This particular model is trained on Japanese web novels.
| Hyperparameter | Value |
|-------------------|--------|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
`*` each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on our Japanese storytelling dataset. Check our blog post for more details.
### How to use
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-jp", torch_dtype=torch.float16, low_cpu_mem_usage=True).eval().cuda()
text = '''あらすじ：あなたは異世界に転生してしまいました。勇者となって、仲間を作り、異世界を冒険しよう！
***
転生すると、ある能力を手に入れていた。それは、'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, temperature=1, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0]
generated_text = tokenizer.decode(last_tokens).replace("�", "")
print("Generation:\n" + generated_text)
```
When run, produces output like this:
```
Generation:
あらすじ：あなたは異世界に転生してしまいました。勇者となって、仲間を作り、異世界を冒険しよう！
***
転生すると、ある能力を手に入れていた。それは、『予知』だ。過去から未来のことを、誰も知らない出来事も含めて見通すことが出来る。
悪魔の欠片と呼ばれる小さな結晶を取り込んで、使役することが出来る。人を惹きつけ、堕落させる。何より、俺は男なんて居なかったし、女に興味もない。……そんなクズの片棒を担ぎ上げる奴が多くなると思うと、ちょっと苦しい。
だが、一部の人間には協力者を得ることが出来る。目立たない街にある寺の中で、常に家に引きこもっている老人。そんなヤツの魂をコントロールすることが出来るのだ。便利な能力だ。しかし、裏切り者は大勢いる。気を抜けば、狂う。だから注意が必要だ。
――「やってやるよ」
アーロンは不敵に笑った。この
```
## Acknowledgements
This project was possible because of the compute provided by the
[TPU Research Cloud](https://sites.research.google/trc/)
Thanks [EleutherAI](https://eleuther.ai/) for pretraining the GPT-J 6B model.
Thanks to everyone who contributed to this project!
- [Finetune](https://github.com/finetuneanon)
- [Aero](https://github.com/AeroScripts)
- [Kurumuz](https://github.com/kurumuz) | {"language": ["ja", "en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"]} | NovelAI/genji-jp | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"ja",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
null | null |
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
Split model has the checkpoints splitted, which makes it use less system RAM while loading and makes it faster to load.
This model needs more effort to set up as you need to install git-lfs and pull the repo.
| Hyperparameter | Value |
|-------------------|--------|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
`*` each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile.
## Training procedure
Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06
## Intended Use
This model is trained for assistence on writing python code and having fun trying weird stuff with it.
### How to use
This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.
For now, you need to use this fork:
[Fork](https://github.com/finetuneanon/transformers)
to install with pip:
```bash
pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
```
**git-lfs** also needs to be installed, on ubuntu:
```bash
apt install git-lfs
```
after it's installed, initialize git-lfs:
```bash
git lfs install
```
then clone this repo:
```bash
git clone https://huggingface.co/NovelAI/genji-python-6B-split
```
Now we can load the model.
We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.
How to use:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GPTNeoForCausalLM,
)
model = AutoModelForCausalLM.from_pretrained("genji-python-6B-split/model").half().eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
text = '''def print_customer_name'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0][len(tokens[0]):]
generated_text = tokenizer.decode(last_tokens)
print("Generation:\n" + generated_text)
```
When ran, this code generates:
```python
Prompt:
def print_customer_name
Generation:
(self, customer):
"""Print the name of a customer."""
if not self.is_valid():
return
print("Customer: {}".format(customer))
```
For example usage, you can see our colab notebook as well:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Eval results
TBD
## Acknowledgements
This project was possible because of the compute provided by the
[TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B.
Thanks to everyone who contributed to this project:
- [Aero](https://github.com/AeroScripts)
- [Finetune](https://github.com/finetuneanon)
- [Kurumuz](https://github.com/kurumuz) | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["the Pile"]} | NovelAI/genji-python-6B-split | null | [
"pytorch",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
| Hyperparameter | Value |
|-------------------|--------|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
`*` each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile.
## Training procedure
Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06
## Intended Use
This model is trained for assistence on writing python code and having fun trying weird stuff with it.
### How to use
This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.
For now, you need to use this fork:
[Fork](https://github.com/finetuneanon/transformers)
to install with pip:
```bash
pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
```
This model takes more than 16 gigs of RAM to load. If you want more efficient and faster loading, please check our split model.
We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.
How to use:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GPTNeoForCausalLM,
)
model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-python-6B", use_auth_token=True).half().eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
text = '''def print_customer_name'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0][len(tokens[0]):]
generated_text = tokenizer.decode(last_tokens)
print("Generation:\n" + generated_text)
```
When ran, this code generates:
```python
Prompt:
def print_customer_name
Generation:
(self, customer):
"""Print the name of a customer."""
if not self.is_valid():
return
print("Customer: {}".format(customer))
```
For example usage, you can see our colab notebook as well:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Eval results
TBD
## Acknowledgements
This project was possible because of the compute provided by the
[TPU Research Cloud](https://sites.research.google/trc/)
and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B.
Thanks to everyone who contributed to this project!
- [Aero](https://github.com/AeroScripts)
- [Finetune](https://github.com/finetuneanon)
- [Kurumuz](https://github.com/kurumuz) | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["the Pile"]} | NovelAI/genji-python-6B | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers |
# bert-base-multilingual-uncased-sentiment
This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks.
## Training data
Here is the number of product reviews we used for finetuning the model:
| Language | Number of reviews |
| -------- | ----------------- |
| English | 150k |
| Dutch | 80k |
| German | 137k |
| French | 140k |
| Italian | 72k |
| Spanish | 50k |
## Accuracy
The finetuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages:
- Accuracy (exact) is the exact match on the number of stars.
- Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer.
| Language | Accuracy (exact) | Accuracy (off-by-1) |
| -------- | ---------------------- | ------------------- |
| English | 67% | 95%
| Dutch | 57% | 93%
| German | 61% | 94%
| French | 59% | 94%
| Italian | 59% | 95%
| Spanish | 58% | 95%
## Contact
In addition to this model, [NLP Town](https://www.nlp.town) offers custom, monolingual sentiment models for many languages and an improved multilingual model through [RapidAPI](https://rapidapi.com/nlp-town-nlp-town-default/api/multilingual-sentiment-analysis2/).
Feel free to contact us for questions, feedback and/or requests for similar models. | {"language": ["en", "nl", "de", "fr", "it", "es"], "license": "mit"} | Noxel/sentiments_multilenguaje | null | [
"transformers",
"bert",
"text-classification",
"en",
"nl",
"de",
"fr",
"it",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2022-03-02T23:29:04+00:00 |
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