Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
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apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - Accuracy: 0.92 - F1: 0.9204
f2b25eaca50e2f556d4c8d3b9ad56b47
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8464 | 1.0 | 250 | 0.3125 | 0.9085 | 0.9061 | | 0.2476 | 2.0 | 500 | 0.2183 | 0.92 | 0.9204 |
05bff148501a445c37ae0d207b8e0919
apache-2.0
['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation']
false
💡GENIUS – generating text using sketches! **基于草稿的文本生成模型** - **Paper: [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://github.com/beyondguo/genius/blob/master/GENIUS_gby_arxiv.pdf)** 💡**GENIUS** is a powerful conditional text generation model using sketches as input, which can fill in the missing contexts for a given **sketch** (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large- scale textual corpus with a novel *reconstruction from sketch* objective using an *extreme and selective masking* strategy, enabling it to generate diverse and high-quality texts given sketches. **Example 1:** - sketch: `__ machine learning __ my research interest __ data science __` - **GENIUS**: `I am a Ph.D. student in machine learning, and my research interest is in data science. I am interested in understanding how humans and machines interact and how we can improve the quality of life for people around the world.` **Example 2:** - sketch: `自然语言处理__谷歌__通用人工智能__` - **GENIUS**: `自然语言处理是谷歌在通用人工智能领域的一个重要研究方向，其目的是为了促进人类智能的发展。 ` **GENIUS** can also be used as a general textual **data augmentation tool** for **various NLP tasks** (including sentiment analysis, topic classification, NER, and QA). ![image-20221119164544165](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/hi-genius.png) - Models hosted in 🤗 Huggingface: **Model variations:** | Model |
a72131b9ab8c0e6c9ba2ae3a0e073f0a
apache-2.0
['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation']
false
params | Language | comment| |------------------------|--------------------------------|-------|---------| | [`genius-large`](https://huggingface.co/beyond/genius-large) | 406M | English | The version used in **paper** (recommend) | | [`genius-large-k2t`](https://huggingface.co/beyond/genius-large-k2t) | 406M | English | keywords-to-text | | [`genius-base`](https://huggingface.co/beyond/genius-base) | 139M | English | smaller version | | [`genius-base-ps`](https://huggingface.co/beyond/genius-base) | 139M | English | pre-trained both in paragraphs and short sentences | | [`genius-base-chinese`](https://huggingface.co/beyond/genius-base-chinese) | 116M | 中文 | 在一千万纯净中文段落上预训练| ![image-20221119191940969](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/202211191919005.png) More Examples: ![image-20221119184950762](https://cdn.jsdelivr.net/gh/beyondguo/mdnice_pictures/typora/202211191849815.png)
b5f9bce73f1958939873a2f5a3aeb935
apache-2.0
['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation']
false
What is a sketch? First, what is a **sketch**? As defined in our paper, a sketch is "key information consisting of textual spans, phrases, or words, concatenated by mask tokens". It's like a draft or framework when you begin to write an article. With GENIUS model, you can input some key elements you want to mention in your wrinting, then the GENIUS model can generate cohrent text based on your sketch. The sketch which can be composed of: - keywords /key-phrases, like `__NLP__AI__computer__science__` - spans, like `Conference on Empirical Methods__submission of research papers__` - sentences, like `I really like machine learning__I work at Google since last year__` - or a mixup!
9f51758433fd2e640a747a387fa5a8b2
apache-2.0
['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation']
false
3. here we go! generated_text = genius(sketch, num_beams=3, do_sample=True, max_length=200)[0]['generated_text'] print(generated_text) ``` Output: ```shell 'The Conference on Empirical Methods welcomes the submission of research papers. Abstracts should be in the form of a paper or presentation. Please submit abstracts to the following email address: eemml.stanford.edu. The conference will be held at Stanford University on April 1618, 2019. The theme of the conference is Deep Learning.' ``` If you have a lot of sketches, you can batch-up your sketches to a Huggingface `Dataset` object, which can be much faster. TODO: we are also building a python package for more convenient use of GENIUS, which will be released in few weeks.
fdf656b1c3c06530aa4d2570f64171dc
apache-2.0
['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation']
false
2. If you have an NLP dataset (e.g. classification) and want to do data augmentation to enlarge your dataset... Please check [genius/augmentation_clf](https://github.com/beyondguo/genius/tree/master/augmentation_clf) and [genius/augmentation_ner_qa](https://github.com/beyondguo/genius/tree/master/augmentation_ner_qa), where we provide ready-to-run scripts for data augmentation for text classification/NER/MRC tasks.
5d5b4ba9583b51248a694a4bdccfafa4
apache-2.0
['GENIUS', 'conditional text generation', 'sketch-based text generation', 'data augmentation']
false
Augmentation Experiments: Data augmentation is an important application for natural language generation (NLG) models, which is also a valuable evaluation of whether the generated text can be used in real applications. - Setting: Low-resource setting, where only n={50,100,200,500,1000} labeled samples are available for training. The below results are the average of all training sizes. - Text Classification Datasets: [HuffPost](https://huggingface.co/datasets/khalidalt/HuffPost), [BBC](https://huggingface.co/datasets/SetFit/bbc-news), [SST2](https://huggingface.co/datasets/glue), [IMDB](https://huggingface.co/datasets/imdb), [Yahoo](https://huggingface.co/datasets/yahoo_answers_topics), [20NG](https://huggingface.co/datasets/newsgroup). - Base classifier: [DistilBERT](https://huggingface.co/distilbert-base-cased) In-distribution (ID) evaluations: | Method | Huff | BBC | Yahoo | 20NG | IMDB | SST2 | avg. | |:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:| | none | 79.17 | **96.16** | 45.77 | 46.67 | 77.87 | 76.67 | 70.39 | | EDA | 79.20 | 95.11 | 45.10 | 46.15 | 77.88 | 75.52 | 69.83 | | BackT | 80.48 | 95.28 | 46.10 | 46.61 | 78.35 | 76.96 | 70.63 | | MLM | 80.04 | 96.07 | 45.35 | 46.53 | 75.73 | 76.61 | 70.06 | | C-MLM | 80.60 | 96.13 | 45.40 | 46.36 | 77.31 | 76.91 | 70.45 | | LAMBADA | 81.46 | 93.74 | 50.49 | 47.72 | 78.22 | 78.31 | 71.66 | | STA | 80.74 | 95.64 | 46.96 | 47.27 | 77.88 | 77.80 | 71.05 | | **GeniusAug** | 81.43 | 95.74 | 49.60 | 50.38 | **80.16** | 78.82 | 72.68 | | **GeniusAug-f** | **81.82** | 95.99 | **50.42** | **50.81** | 79.40 | **80.57** | **73.17** | Out-of-distribution (OOD) evaluations: | | Huff->BBC | BBC->Huff | IMDB->SST2 | SST2->IMDB | avg. | |------------|:----------:|:----------:|:----------:|:----------:|:----------:| | none | 62.32 | 62.00 | 74.37 | 73.11 | 67.95 | | EDA | 67.48 | 58.92 | 75.83 | 69.42 | 67.91 | | BackT | 67.75 | 63.10 | 75.91 | 72.19 | 69.74 | | MLM | 66.80 | 65.39 | 73.66 | 73.06 | 69.73 | | C-MLM | 64.94 | **67.80** | 74.98 | 71.78 | 69.87 | | LAMBADA | 68.57 | 52.79 | 75.24 | 76.04 | 68.16 | | STA | 69.31 | 64.82 | 74.72 | 73.62 | 70.61 | | **GeniusAug** | 74.87 | 66.85 | 76.02 | 74.76 | 73.13 | | **GeniusAug-f** | **76.18** | 66.89 | **77.45** | **80.36** | **75.22** |
e4d0460a95cac1eb15bfa56f4aee9c63
mit
['generated_from_keras_callback']
false
alarm_prediction_tokenizer3 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6252 - Validation Loss: 0.5814 - Epoch: 5
46ce957cc9833f2f94d9ab445256e0d7
mit
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16
f54ce99ca4d38c5e06b5b0e3b7b6b9cb
mit
['generated_from_keras_callback']
false
Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9339 | 1.3070 | 0 | | 1.1890 | 0.9436 | 1 | | 0.9039 | 0.7802 | 2 | | 0.7734 | 0.6915 | 3 | | 0.6879 | 0.6274 | 4 | | 0.6252 | 0.5814 | 5 |
0500c47142dd4fb162da4f7ae206a8eb
apache-2.0
['setfit', 'sentence-transformers', 'text-classification']
false
fathyshalab/domain_transfer_general-massive_music-roberta-large-v1-5-7 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer.
72a43212a8f793da640c15acf8df1001
apache-2.0
[]
false
How to use the generator in `transformers` ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="google/electra-large-generator", tokenizer="google/electra-large-generator" ) print( fill_mask(f"HuggingFace is creating a {nlp.tokenizer.mask_token} that the community uses to solve NLP tasks.") ) ```
c8d1b0b081e1c51cb74f05661ec67c34
apache-2.0
['generated_from_trainer']
false
wav2vec2-base-cynthia-tedlium-2500-v2 This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6425 - Wer: 0.2033
582376730b45eb3ce7459676865d16d5
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 50 - mixed_precision_training: Native AMP
7b6e7bbe18986d201aaf2f5a9a03df3c
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1196 | 6.58 | 500 | 0.6498 | 0.2103 | | 0.1176 | 13.16 | 1000 | 0.6490 | 0.2169 | | 0.1227 | 19.73 | 1500 | 0.6241 | 0.2127 | | 0.1078 | 26.31 | 2000 | 0.6359 | 0.2118 | | 0.0956 | 32.89 | 2500 | 0.6330 | 0.2073 | | 0.1008 | 39.47 | 3000 | 0.6816 | 0.2036 | | 0.09 | 46.05 | 3500 | 0.6425 | 0.2033 |
8bf74f7175b7098afdeed5d51eee4212
mit
['generation', 'math learning', 'education']
false
SafeMathBot for NLP tasks in math learning environments This model is fine-tuned with GPT2-xl with 8 Nvidia RTX 1080Ti GPUs and enhanced with conversation safety policies (e.g., threat, profanity, identity attack) using 3,000,000 math discussion posts by students and facilitators on Algebra Nation (https://www.mathnation.com/). SafeMathBot consists of 48 layers and over 1.5 billion parameters, consuming up to 6 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively avoid unsafe response generation. It was trained to allow researchers to control generated responses' safety using tags `[SAFE]` and `[UNSAFE]`
004338bee05ce9716fd13f7643373632
mit
['generation', 'math learning', 'education']
false
A list of special tokens the model was trained with special_tokens_dict = { 'additional_special_tokens': [ '[SAFE]','[UNSAFE]', '[OK]', '[SELF_M]','[SELF_F]', '[SELF_N]', '[PARTNER_M]', '[PARTNER_F]', '[PARTNER_N]', '[ABOUT_M]', '[ABOUT_F]', '[ABOUT_N]', '<speaker1>', '<speaker2>' ], 'bos_token': '<bos>', 'eos_token': '<eos>', } from transformers import AutoTokenizer, AutoModelForCausalLM math_bot_tokenizer = AutoTokenizer.from_pretrained('uf-aice-lab/SafeMathBot') safe_math_bot = AutoModelForCausalLM.from_pretrained('uf-aice-lab/SafeMathBot') text = "Replace me by any text you'd like." encoded_input = math_bot_tokenizer(text, return_tensors='pt') output = safe_math_bot(**encoded_input) ```
c17a49ffeb7d741007214e2dd9907eba
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Whisper Small ML - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2308 - Wer: 36.7397
0de26edfe819cf8f7214d7d581e5b001
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP
182a03e65e7793b60d30e8070a1fdc9e
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1275 | 4.03 | 500 | 0.1630 | 35.4015 | | 0.09 | 9.02 | 1000 | 0.1821 | 40.0243 | | 0.062 | 14.01 | 1500 | 0.2004 | 37.7129 | | 0.0441 | 19.0 | 2000 | 0.2105 | 36.2530 | | 0.0335 | 23.03 | 2500 | 0.2250 | 37.7129 | | 0.0276 | 28.02 | 3000 | 0.2308 | 36.7397 |
75507d791ba1b29afc9d562fc9cca4b1
apache-2.0
['translation']
false
opus-mt-fi-tll * source languages: fi * target languages: tll * OPUS readme: [fi-tll](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-tll/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-tll/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-tll/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-tll/opus-2020-01-24.eval.txt)
a8c49d7a6bf9ef9b519eb3fdc13f822e
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5447 - Matthews Correlation: 0.5470
f261241bec3ab535c8df05b006d56a1e
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5249 | 1.0 | 535 | 0.5159 | 0.4004 | | 0.3458 | 2.0 | 1070 | 0.5198 | 0.4738 | | 0.2349 | 3.0 | 1605 | 0.5447 | 0.5470 | | 0.1773 | 4.0 | 2140 | 0.7828 | 0.5185 | | 0.1245 | 5.0 | 2675 | 0.8306 | 0.5279 |
571dae0acbf8bb0ede0ed4d1f747a890
apache-2.0
['generated_from_trainer']
false
ner-from-bert This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0615 - Precision: 0.9351 - Recall: 0.9504 - F1: 0.9427 - Accuracy: 0.9859
d671f8fcaa3ce4a41e5f4d055b7f1019
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0879 | 1.0 | 1756 | 0.0685 | 0.9170 | 0.9320 | 0.9245 | 0.9815 | | 0.0328 | 2.0 | 3512 | 0.0625 | 0.9267 | 0.9495 | 0.9380 | 0.9853 | | 0.0189 | 3.0 | 5268 | 0.0615 | 0.9351 | 0.9504 | 0.9427 | 0.9859 |
a6b573318f75b3706d06dfec5a9dbba1
mit
['generated_from_trainer', 'nlu', 'domain-classificatoin']
false
xlm-r-base-amazon-massive-domain This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset). It achieves the following results on the evaluation set: - Loss: 0.3788 - Accuracy: 0.9213 - F1: 0.9213
ee0dfd4a6f3f770a9356bb3c64738ace
mit
['generated_from_trainer', 'nlu', 'domain-classificatoin']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.382 | 1.0 | 720 | 0.4533 | 0.8795 | 0.8795 | | 0.4598 | 2.0 | 1440 | 0.3448 | 0.9026 | 0.9026 | | 0.2547 | 3.0 | 2160 | 0.3762 | 0.9065 | 0.9065 | | 0.1986 | 4.0 | 2880 | 0.3748 | 0.9139 | 0.9139 | | 0.1358 | 5.0 | 3600 | 0.3788 | 0.9213 | 0.9213 |
f734f790731c9f380834dd9ae5781c49
apache-2.0
['generated_from_trainer']
false
bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0641 - Precision: 0.9290 - Recall: 0.9475 - F1: 0.9382 - Accuracy: 0.9858
a3d1920bb8fbf19f83390d4891a90553
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0867 | 1.0 | 1756 | 0.0716 | 0.9102 | 0.9297 | 0.9198 | 0.9820 | | 0.0345 | 2.0 | 3512 | 0.0680 | 0.9290 | 0.9465 | 0.9376 | 0.9854 | | 0.0191 | 3.0 | 5268 | 0.0641 | 0.9290 | 0.9475 | 0.9382 | 0.9858 |
4af1bb8c56aa3e11327a52c0a3fec77d
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-distilled-squad-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on the squad_v2 dataset.
02c6c7e22c7b737466cc6e7ee207b7d6
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.1
0386d49c424957f1778599b16d4a8b20
mit
[]
false
Jeffzo3 on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
ae52156d55929f164c905df64b3937dd
mit
[]
false
Model by JeffZ This your the Stable Diffusion model fine-tuned the Jeffzo3 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept:
7df7a49f6948271bd09ac86b23fc6f05
apache-2.0
['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_120k']
false
MultiBERTs, Intermediate Checkpoint - Seed 2, Step 120k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model
9a13e1b36df5705538271616f4a0ff79
apache-2.0
['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_120k']
false
How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_120k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_120k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_120k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```
0fce875b056017246b38c2b341752b76
apache-2.0
['generated_from_trainer']
false
wav2vec2-base-stac-msa-local This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0671 - Wer: 0.7924 - Cer: 0.3289
c13b946846cbe6a0ff3330cb6423c036
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30
87de80aee3dfafec103c32a26223e66e
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:| | 1.4697 | 1.0 | 3773 | 1.8242 | 0.9395 | 0.5135 | | 1.1644 | 2.0 | 7546 | 1.6306 | 0.8731 | 0.4446 | | 0.9517 | 3.0 | 11319 | 1.4122 | 0.8587 | 0.4059 | | 0.8563 | 4.0 | 15092 | 1.5409 | 0.8386 | 0.4034 | | 0.7556 | 5.0 | 18865 | 1.4103 | 0.8247 | 0.3724 | | 0.6841 | 6.0 | 22638 | 1.4608 | 0.8166 | 0.3735 | | 0.5834 | 7.0 | 26411 | 1.5139 | 0.8113 | 0.3646 | | 0.5607 | 8.0 | 30184 | 1.5303 | 0.8263 | 0.3797 | | 0.5442 | 9.0 | 33957 | 1.3824 | 0.8198 | 0.3476 | | 0.4584 | 10.0 | 37730 | 1.6412 | 0.8160 | 0.3576 | | 0.4257 | 11.0 | 41503 | 1.5575 | 0.8003 | 0.3514 | | 0.3631 | 12.0 | 45276 | 1.5776 | 0.8141 | 0.3454 | | 0.3272 | 13.0 | 49049 | 1.5124 | 0.8127 | 0.3399 | | 0.3348 | 14.0 | 52822 | 1.6733 | 0.7946 | 0.3398 | | 0.3231 | 15.0 | 56595 | 1.5154 | 0.7987 | 0.3324 | | 0.2556 | 16.0 | 60368 | 1.6161 | 0.7993 | 0.3402 | | 0.238 | 17.0 | 64141 | 1.6126 | 0.7974 | 0.3329 | | 0.2228 | 18.0 | 67914 | 1.7419 | 0.8014 | 0.3291 | | 0.2129 | 19.0 | 71687 | 1.8394 | 0.8015 | 0.3374 | | 0.1975 | 20.0 | 75460 | 1.9307 | 0.7928 | 0.3451 | | 0.1981 | 21.0 | 79233 | 1.8700 | 0.8080 | 0.3375 | | 0.1628 | 22.0 | 83006 | 1.9776 | 0.8061 | 0.3408 | | 0.1462 | 23.0 | 86779 | 1.9090 | 0.8031 | 0.3306 | | 0.1555 | 24.0 | 90552 | 1.9063 | 0.7878 | 0.3294 | | 0.1515 | 25.0 | 94325 | 1.9632 | 0.7963 | 0.3278 | | 0.1194 | 26.0 | 98098 | 1.9280 | 0.7991 | 0.3301 | | 0.1219 | 27.0 | 101871 | 2.0248 | 0.7927 | 0.3329 | | 0.1184 | 28.0 | 105644 | 2.0447 | 0.7903 | 0.3314 | | 0.074 | 29.0 | 109417 | 2.0513 | 0.7910 | 0.3287 | | 0.0836 | 30.0 | 113190 | 2.0671 | 0.7924 | 0.3289 |
c280f782327b819b333bbab48ef5fc30
apache-2.0
['generated_from_trainer']
false
mt5-base-finetuned-xsum-mlsum___topic_text_google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.1582 - Rouge2: 0.0133 - Rougel: 0.1585 - Rougelsum: 0.1586 - Gen Len: 10.2326
6f79716e674293941a3d7474072d46bd
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 66592 | nan | 0.1582 | 0.0133 | 0.1585 | 0.1586 | 10.2326 |
31c76ff4893caca5e242f55c31d9f1cd
apache-2.0
['generated_from_trainer']
false
mnli_bert-base-uncased_81 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4882 - Accuracy: 0.8207
dd1d4124a1d1876a2205689f0a96565a
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 400 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0
b659661a17087c1105a0a980924943b5
apache-2.0
['translation']
false
opus-mt-is-sv * source languages: is * target languages: sv * OPUS readme: [is-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/is-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/is-sv/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/is-sv/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/is-sv/opus-2020-01-09.eval.txt)
3cc12216b419853b69965620f7399cb7
apache-2.0
['argumentation']
false
Generate reasons that support a claim This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks. Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review.
c3ea47e8aa34b264322e79bb0cdd11f3
apache-2.0
['Italian', 'efficient', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation']
false
IT5 Cased Small Efficient EL32 for Question Generation 💭 🇮🇹 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) as part of the experiments of the paper [IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation](https://arxiv.org/abs/2203.03759) by [Gabriele Sarti](https://gsarti.com) and [Malvina Nissim](https://malvinanissim.github.io). Efficient IT5 models differ from the standard ones by adopting a different vocabulary that enables cased text generation and an [optimized model architecture](https://arxiv.org/abs/2109.10686) to improve performances while reducing parameter count. The Small-EL32 replaces the original encoder from the T5 Small architecture with a 32-layer deep encoder, showing improved performances over the base model. A comprehensive overview of other released materials is provided in the [gsarti/it5](https://github.com/gsarti/it5) repository. Refer to the paper for additional details concerning the reported scores and the evaluation approach.
aa28bc696eff52fc2bd078079ed620f7
apache-2.0
['Italian', 'efficient', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation']
false
Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines qg = pipeline("text2text-generation", model='it5/it5-efficient-small-el32-question-generation') qg("Le conoscenze mediche erano stagnanti durante il Medioevo. Il resoconto più autorevole di allora è venuto dalla facoltà di medicina di Parigi in un rapporto al re di Francia che ha incolpato i cieli, sotto forma di una congiunzione di tre pianeti nel 1345 che causò una "grande pestilenza nell\' aria". Questa relazione è diventata la prima e più diffusa di una serie di casi di peste che cercava di dare consigli ai malati. Che la peste fosse causata dalla cattiva aria divenne la teoria più accettata. Oggi, questo è conosciuto come la teoria di Miasma. La parola "peste" non aveva un significato particolare in questo momento, e solo la ricorrenza dei focolai durante il Medioevo gli diede il nome che è diventato il termine medico. Risposta: re di Francia") >>> [{"generated_text": "Per chi è stato redatto il referto medico?"}] ``` or loaded using autoclasses: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("it5/it5-efficient-small-el32-question-generation") model = AutoModelForSeq2SeqLM.from_pretrained("it5/it5-efficient-small-el32-question-generation") ``` If you use this model in your research, please cite our work as: ```bibtex @article{sarti-nissim-2022-it5, title={{IT5}: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } ```
d82364dc44ebe5ea5367b80915b7d032
apache-2.0
['Italian', 'efficient', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7.0
77d0146fcf166ea83a5e38f8c747b83d
cc-by-4.0
['question generation']
false
Model Card of `lmqg/bart-base-squad-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
71855be8202ecf563c0951c055649a18
cc-by-4.0
['question generation']
false
model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-base-squad-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ```
af4eb0aea55048d1893fa23e33856344
cc-by-4.0
['question generation']
false
Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 56.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 40.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 31.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 24.68 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 26.05 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 64.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 52.66 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 95.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 70.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 95.55 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 70.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 95.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 70.1 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/bart-base-squad-ae`](https://huggingface.co/lmqg/bart-base-squad-ae). [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_bart-base-squad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 92.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 64.24 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 92.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 64.46 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 92.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 64.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | - ***Metrics (Question Generation, Out-of-Domain)*** | Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link | |:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:| | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 90.49 | 5.82 | 21.27 | 60.27 | 23.82 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 93.07 | 10.73 | 26.23 | 65.67 | 28.44 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 92.36 | 7.65 | 24.43 | 63.69 | 23.9 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 90.57 | 5.38 | 20.4 | 60.14 | 21.41 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 87.75 | 0.0 | 11.52 | 55.21 | 10.77 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 87.6 | 0.0 | 14.87 | 56.07 | 14.29 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 87.38 | 0.6 | 15.53 | 56.63 | 12.49 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 87.73 | 1.08 | 12.86 | 55.55 | 13.9 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 87.71 | 0.0 | 11.47 | 54.91 | 12.16 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 88.78 | 1.02 | 13.92 | 55.91 | 13.41 | [link](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) |
6eb328b94da7a10acfa64ae1ad430e11
cc-by-4.0
['question generation']
false
Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 7 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/trainer_config.json).
ff95ec661bf04a0430b0a0b6cdb952a9
creativeml-openrail-m
['text-to-image']
false
elonmusk01 Dreambooth model trained by cormacncheese with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept:
fe2965228524fb8fc6d6819f73af58e6
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2200 - Accuracy: 0.929 - F1: 0.9292
d4546465379f5e24e57d8f902bdfd42b
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8358 | 1.0 | 250 | 0.3190 | 0.908 | 0.9050 | | 0.2551 | 2.0 | 500 | 0.2200 | 0.929 | 0.9292 |
aef27f2304f7e06693c77d8b69d9466c
mit
[]
false
Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |-----------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('transformation', ColumnTransformer(transformers=[('loading_missing_value_imputer',<br /> SimpleImputer(), ['loading']),<br /> ('numerical_missing_value_imputer',<br /> SimpleImputer(),<br /> ['loading', 'measurement_3', 'measurement_4',<br /> 'measurement_5', 'measurement_6',<br /> 'measurement_7', 'measurement_8',<br /> 'measurement_9', 'measurement_10',<br /> 'measurement_11', 'measurement_12',<br /> 'measurement_13', 'measurement_14',<br /> 'measurement_15', 'measurement_16',<br /> 'measurement_17']),<br /> ('attribute_0_encoder', OneHotEncoder(),<br /> ['attribute_0']),<br /> ('attribute_1_encoder', OneHotEncoder(),<br /> ['attribute_1']),<br /> ('product_code_encoder', OneHotEncoder(),<br /> ['product_code'])])), ('model', DecisionTreeClassifier(max_depth=4))] | | verbose | False | | transformation | ColumnTransformer(transformers=[('loading_missing_value_imputer',<br /> SimpleImputer(), ['loading']),<br /> ('numerical_missing_value_imputer',<br /> SimpleImputer(),<br /> ['loading', 'measurement_3', 'measurement_4',<br /> 'measurement_5', 'measurement_6',<br /> 'measurement_7', 'measurement_8',<br /> 'measurement_9', 'measurement_10',<br /> 'measurement_11', 'measurement_12',<br /> 'measurement_13', 'measurement_14',<br /> 'measurement_15', 'measurement_16',<br /> 'measurement_17']),<br /> ('attribute_0_encoder', OneHotEncoder(),<br /> ['attribute_0']),<br /> ('attribute_1_encoder', OneHotEncoder(),<br /> ['attribute_1']),<br /> ('product_code_encoder', OneHotEncoder(),<br /> ['product_code'])]) | | model | DecisionTreeClassifier(max_depth=4) | | transformation__n_jobs | | | transformation__remainder | drop | | transformation__sparse_threshold | 0.3 | | transformation__transformer_weights | | | transformation__transformers | [('loading_missing_value_imputer', SimpleImputer(), ['loading']), ('numerical_missing_value_imputer', SimpleImputer(), ['loading', 'measurement_3', 'measurement_4', 'measurement_5', 'measurement_6', 'measurement_7', 'measurement_8', 'measurement_9', 'measurement_10', 'measurement_11', 'measurement_12', 'measurement_13', 'measurement_14', 'measurement_15', 'measurement_16', 'measurement_17']), ('attribute_0_encoder', OneHotEncoder(), ['attribute_0']), ('attribute_1_encoder', OneHotEncoder(), ['attribute_1']), ('product_code_encoder', OneHotEncoder(), ['product_code'])] | | transformation__verbose | False | | transformation__verbose_feature_names_out | True | | transformation__loading_missing_value_imputer | SimpleImputer() | | transformation__numerical_missing_value_imputer | SimpleImputer() | | transformation__attribute_0_encoder | OneHotEncoder() | | transformation__attribute_1_encoder | OneHotEncoder() | | transformation__product_code_encoder | OneHotEncoder() | | transformation__loading_missing_value_imputer__add_indicator | False | | transformation__loading_missing_value_imputer__copy | True | | transformation__loading_missing_value_imputer__fill_value | | | transformation__loading_missing_value_imputer__missing_values | nan | | transformation__loading_missing_value_imputer__strategy | mean | | transformation__loading_missing_value_imputer__verbose | 0 | | transformation__numerical_missing_value_imputer__add_indicator | False | | transformation__numerical_missing_value_imputer__copy | True | | transformation__numerical_missing_value_imputer__fill_value | | | transformation__numerical_missing_value_imputer__missing_values | nan | | transformation__numerical_missing_value_imputer__strategy | mean | | transformation__numerical_missing_value_imputer__verbose | 0 | | transformation__attribute_0_encoder__categories | auto | | transformation__attribute_0_encoder__drop | | | transformation__attribute_0_encoder__dtype | <class 'numpy.float64'> | | transformation__attribute_0_encoder__handle_unknown | error | | transformation__attribute_0_encoder__sparse | True | | transformation__attribute_1_encoder__categories | auto | | transformation__attribute_1_encoder__drop | | | transformation__attribute_1_encoder__dtype | <class 'numpy.float64'> | | transformation__attribute_1_encoder__handle_unknown | error | | transformation__attribute_1_encoder__sparse | True | | transformation__product_code_encoder__categories | auto | | transformation__product_code_encoder__drop | | | transformation__product_code_encoder__dtype | <class 'numpy.float64'> | | transformation__product_code_encoder__handle_unknown | error | | transformation__product_code_encoder__sparse | True | | model__ccp_alpha | 0.0 | | model__class_weight | | | model__criterion | gini | | model__max_depth | 4 | | model__max_features | | | model__max_leaf_nodes | | | model__min_impurity_decrease | 0.0 | | model__min_samples_leaf | 1 | | model__min_samples_split | 2 | | model__min_weight_fraction_leaf | 0.0 | | model__random_state | | | model__splitter | best | </details>
a094c42d54a13a59ce6ae68f60234ca7
mit
[]
false
sk-8bc9e9e7-93eb-4a71-9ad5-6d31c0b7f893 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}
d7767c37958fa05637b0a80275ce45e0
mit
[]
false
sk-8bc9e9e7-93eb-4a71-9ad5-6d31c0b7f893 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}
de364ad40166a10af3f48027b82492ef
mit
[]
false
sk-8bc9e9e7-93eb-4a71-9ad5-6d31c0b7f893 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}
10e4806f4620c50af144688daf2030f8
mit
[]
false
sk-8bc9e9e7-93eb-4a71-9ad5-6d31c0b7f893 div.sk-text-repr-fallback {display: none;}</style><div id="sk-8bc9e9e7-93eb-4a71-9ad5-6d31c0b7f893" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&
94e7cb1edd5234e0ca0cd447b46d93e3
mit
[]
false
x27;, DecisionTreeClassifier(max_depth=4))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f3a0413c-728e-4fd9-bbd8-5c6ec5312931" type="checkbox" ><label for="f3a0413c-728e-4fd9-bbd8-5c6ec5312931" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&
2b3c39d4354bf360287a087de2b0c131
mit
[]
false
x27;, DecisionTreeClassifier(max_depth=4))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3f892f74-5115-4ab0-9c64-f760f11a7cbe" type="checkbox" ><label for="3f892f74-5115-4ab0-9c64-f760f11a7cbe" class="sk-toggleable__label sk-toggleable__label-arrow">transformation: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&
063259ddbcfc2ba2443cc9b407b842e2
mit
[]
false
x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="ec9bebf9-8c02-4785-974c-0e727c4449c0" type="checkbox" ><label for="ec9bebf9-8c02-4785-974c-0e727c4449c0" class="sk-toggleable__label sk-toggleable__label-arrow">loading_missing_value_imputer</label><div class="sk-toggleable__content"><pre>[&
5c4d1f819b3433d13f15f6d4806c72f3
mit
[]
false
x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="572cc9df-a4bb-49b4-b730-d012d99ba876" type="checkbox" ><label for="572cc9df-a4bb-49b4-b730-d012d99ba876" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c6058039-3e65-4724-ad03-96517a382ad6" type="checkbox" ><label for="c6058039-3e65-4724-ad03-96517a382ad6" class="sk-toggleable__label sk-toggleable__label-arrow">numerical_missing_value_imputer</label><div class="sk-toggleable__content"><pre>[&
de299e8d46d73dfeaa1c2805543ca133
mit
[]
false
x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d385b0fd-dfaf-490c-8fda-dc024393a022" type="checkbox" ><label for="d385b0fd-dfaf-490c-8fda-dc024393a022" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="54db5302-69ab-49a1-b939-cb94c0958ab3" type="checkbox" ><label for="54db5302-69ab-49a1-b939-cb94c0958ab3" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_0_encoder</label><div class="sk-toggleable__content"><pre>[&
e7e45c1526ff160ff84548fc62398aad
mit
[]
false
x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="c0a718c8-7093-4d45-85ae-847bfac3ec7e" type="checkbox" ><label for="c0a718c8-7093-4d45-85ae-847bfac3ec7e" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="993a1233-2b0d-473e-9bb3-f7c9d0bc654a" type="checkbox" ><label for="993a1233-2b0d-473e-9bb3-f7c9d0bc654a" class="sk-toggleable__label sk-toggleable__label-arrow">attribute_1_encoder</label><div class="sk-toggleable__content"><pre>[&
39a4617c58d1e14a2c011a3adc7c79c4
mit
[]
false
x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4311756e-5a71-45ce-9005-a1e5448b1c30" type="checkbox" ><label for="4311756e-5a71-45ce-9005-a1e5448b1c30" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9bfb54df-7509-4669-b6e7-db3520c2d1c4" type="checkbox" ><label for="9bfb54df-7509-4669-b6e7-db3520c2d1c4" class="sk-toggleable__label sk-toggleable__label-arrow">product_code_encoder</label><div class="sk-toggleable__content"><pre>[&
779879311ffae954b5c96b4674a68ad0
mit
[]
false
x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="1acc88d7-a436-40f6-99a3-ebfbbc9f897a" type="checkbox" ><label for="1acc88d7-a436-40f6-99a3-ebfbbc9f897a" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="5626883d-68bc-41b4-8913-23b6aed62eb8" type="checkbox" ><label for="5626883d-68bc-41b4-8913-23b6aed62eb8" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(max_depth=4)</pre></div></div></div></div></div></div></div>
78b6fba750c7256b93fedde659d53c45
mit
[]
false
Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |-------------------|-----------| | C | 1.0 | | class_weight | | | dual | False | | fit_intercept | True | | intercept_scaling | 1 | | l1_ratio | | | max_iter | 100 | | multi_class | auto | | n_jobs | | | penalty | l2 | | random_state | 0 | | solver | liblinear | | tol | 0.0001 | | verbose | 0 | | warm_start | False | </details>
8152faee435f7f501c7aa46009440d39
mit
[]
false
sk-9e32ec08-a06c-47ad-ba8c-72228d2a4022 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}
03307ac4ada97bb601f5d1801af3cd3d
mit
[]
false
sk-9e32ec08-a06c-47ad-ba8c-72228d2a4022 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}
31d737ae0d958a31f70dcac41d2b1700
mit
[]
false
sk-9e32ec08-a06c-47ad-ba8c-72228d2a4022 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}
4e0a20c9524de2c38fbcd21d68b050cb
mit
[]
false
sk-9e32ec08-a06c-47ad-ba8c-72228d2a4022 div.sk-text-repr-fallback {display: none;}</style><div id="sk-9e32ec08-a06c-47ad-ba8c-72228d2a4022" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=0, solver=&
3543e1489f6464d3afaec0ce68c1715a
mit
[]
false
x27;)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="51d3cd4d-ea90-43e3-8d6a-5abc1df508b6" type="checkbox" checked><label for="51d3cd4d-ea90-43e3-8d6a-5abc1df508b6" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(random_state=0, solver=&
144d437f3926f7ac012edc9e10ac8176
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Wav2Vec2-Large-XLSR-53-Assamese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Assamese using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz.
b947cedf8acec9ecb3a19bd276129ee3
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
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", "as", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-as") model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-as") resampler = torchaudio.transforms.Resample(48_000, 16_000)
e4707d6869aa7033f29ddcef11f99829
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Evaluation The model can be evaluated as follows on the Assamese 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", "as", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anuragshas/wav2vec2-large-xlsr-as") model = Wav2Vec2ForCTC.from_pretrained("anuragshas/wav2vec2-large-xlsr-as") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\”\\়\\।]' resampler = torchaudio.transforms.Resample(48_000, 16_000)
71d597fca476b38516e92d4a18ebca88
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub('’ ',' ',batch["sentence"]) batch["sentence"] = re.sub(' ‘',' ',batch["sentence"]) batch["sentence"] = re.sub('’|‘','\'',batch["sentence"]) 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)
6aee9dcb4dcfb05a64dc946877848db4
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
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) 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**: 69.63 %
cce4b7c691273a0c6bb250b28926a7f7
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1476
239687cf2066c9a0605365eede3d4c64
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2071 | 1.0 | 5533 | 1.1445 | | 0.9549 | 2.0 | 11066 | 1.1221 | | 0.7506 | 3.0 | 16599 | 1.1476 |
98073aa564e93da8cf795374574f012d
mit
['generated_from_trainer']
false
elegant_liskov This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
42c35fb7f4c12e431b5f0efb01626d9b
mit
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'elegant_liskov', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
02d6514d482b2d78ef8d70a080b76029
mit
['speech', 'text', 'cross-modal', 'unified model', 'self-supervised learning', 'SpeechT5', 'Text-to-Speech']
false
SpeechT5 TTS Manifest | [**Github**](https://github.com/microsoft/SpeechT5) | [**Huggingface**](https://huggingface.co/mechanicalsea/speecht5-tts) | This manifest is an attempt to recreate the Text-to-Speech recipe used for training [SpeechT5](https://aclanthology.org/2022.acl-long.393). This manifest was constructed using [LibriTTS](http://www.openslr.org/60/) clean datasets, including train-clean-100 and train-clean-360 for training, dev-clean for validation, and test-clean for evaluation. The test-clean-200 contains 200 utterances id for the mean option score (MOS), and the comparison mean option score (CMOS).
f89fd0cd44e5c7212924b08d2bca0f8f
mit
['speech', 'text', 'cross-modal', 'unified model', 'self-supervised learning', 'SpeechT5', 'Text-to-Speech']
false
News - 8 February 2023: SpeechT5 is integrated as an official model into the Hugging Face Transformers library [[Blog](https://huggingface.co/blog/speecht5)] and [[Demo](https://huggingface.co/spaces/Matthijs/speecht5-tts-demo)].
026c39d0e647e5e9866dcab23d6dc05e
mit
['speech', 'text', 'cross-modal', 'unified model', 'self-supervised learning', 'SpeechT5', 'Text-to-Speech']
false
Requirements - [SpeechBrain](https://github.com/speechbrain/speechbrain) for extracting speaker embedding - [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN) for implementing vocoder.
547d715ed6fa724e604f813351106c8b
mit
['speech', 'text', 'cross-modal', 'unified model', 'self-supervised learning', 'SpeechT5', 'Text-to-Speech']
false
Model and Samples - [`speecht5_tts.pt`](./speecht5_tts.pt) are reimplemented Text-to-Speech fine-tuning on the released manifest **but with a smaller batch size or max updates** (Ensure the manifest is ok). - `samples` are created by the released fine-tuned model and vocoder.
9014b38e07a832d7c98c177bd6a3392f
mit
['speech', 'text', 'cross-modal', 'unified model', 'self-supervised learning', 'SpeechT5', 'Text-to-Speech']
false
Reference If you find our work is useful in your research, please cite the following paper: ```bibtex @inproceedings{ao-etal-2022-speecht5, title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing}, author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {May}, year = {2022}, pages={5723--5738}, } ```
fde91ecf5fc1ac1b02c49345e37eea3a
apache-2.0
['automatic-speech-recognition', 'th']
false
exp_w2v2t_th_hubert_s817 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on Thai using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
aed2aabb95931b7f8e3e741cb8c1f9eb
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Whisper Large V2 Hindi - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1787 - Wer: 10.2486
f29e467fa4571e1c7ee8ae9e2ec3160c
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 2000 - mixed_precision_training: Native AMP
093a55c348493534a32fca968d884b45
apache-2.0
['whisper-event', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0238 | 2.44 | 2000 | 0.1787 | 10.2486 |
d7a750a26da3f30563ee4582a1c65f84
apache-2.0
['generated_from_trainer']
false
distilroberta-base-mic This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3435 - Accuracy: 0.9104 - F1: 0.9103
66107b0d41ee8ebbad715ea72c051d50
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8.748413056668156e-05 - train_batch_size: 200 - eval_batch_size: 200 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4
1e1d121bb3b88329412751c5e0a1cfa8
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 120 | 0.2830 | 0.8804 | 0.8797 | | No log | 2.0 | 240 | 0.2398 | 0.9046 | 0.9046 | | No log | 3.0 | 360 | 0.3474 | 0.8959 | 0.8954 | | No log | 4.0 | 480 | 0.3435 | 0.9104 | 0.9103 |
4d41d74fee07b9e7fb2aebbd38322c2a
apache-2.0
['generated_from_keras_callback']
false
ranguis/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-swc-fr](https://huggingface.co/Helsinki-NLP/opus-mt-swc-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5054 - Train Accuracy: 0.3469 - Validation Loss: 2.8945 - Validation Accuracy: 0.5309 - Epoch: 0
b3f05d9d44bda571fc9fcb15fc881c7b
apache-2.0
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 12, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16
026c198f19497e29668dc709fd8c3f0d
apache-2.0
['generated_from_keras_callback']
false
Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 3.5054 | 0.3469 | 2.8945 | 0.5309 | 0 |
8374f1702ff6f6fba339218013c12802
apache-2.0
['generated_from_trainer']
false
bert-base-uncased-finetuned-QnA-v1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7610
e19862844edd989cd39735f7643f4c79
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20
a0201d4066ea41f79ec192504ed58ce1