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train · 883k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-15 18:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 18:28:34	card stringlengths 11 1.01M
google/byt5-small	google	2023-01-24T16:36:59Z	1,451,674	64	transformers	[ "transformers", "pytorch", "tf", "jax", "t5", "text2text-generation", "multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:mc4", "arxiv:1907.06292", "arxiv:2105.13626", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- # ByT5 - Small ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-small). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,e.g., `google/byt5-small` significantly outperforms [mt5-small](https://huggingface.co/google/mt5-small) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-small') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens loss = model(input_ids, labels=labels).loss # forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-small') tokenizer = AutoTokenizer.from_pretrained('google/byt5-small') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss # forward pass ``` ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/ByT5.png)
google/byt5-base	google	2023-01-24T16:36:53Z	32,951	21	transformers	[ "transformers", "pytorch", "tf", "jax", "t5", "text2text-generation", "multilingual", "af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lb", "lo", "lt", "lv", "mg", "mi", "mk", "ml", "mn", "mr", "ms", "mt", "my", "ne", "nl", "no", "ny", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "sm", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "ta", "te", "tg", "th", "tr", "uk", "und", "ur", "uz", "vi", "xh", "yi", "yo", "zh", "zu", "dataset:mc4", "arxiv:1907.06292", "arxiv:2105.13626", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- # ByT5 - Base ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-base). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,e.g., `google/byt5-base` significantly outperforms [mt5-base](https://huggingface.co/google/mt5-base) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-base') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens loss = model(input_ids, labels=labels).loss # forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-base') tokenizer = AutoTokenizer.from_pretrained('google/byt5-base') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss # forward pass ``` ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/ByT5.png)
google/bigbird-pegasus-large-bigpatent	google	2023-01-24T16:36:44Z	785	39	transformers	[ "transformers", "pytorch", "bigbird_pegasus", "text2text-generation", "summarization", "en", "dataset:big_patent", "arxiv:2007.14062", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- language: en license: apache-2.0 datasets: - big_patent tags: - summarization --- # BigBirdPegasus model (large) BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was introduced in this [paper](https://arxiv.org/abs/2007.14062) and first released in this [repository](https://github.com/google-research/bigbird). Disclaimer: The team releasing BigBird did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BigBird relies on block sparse attention instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. ## How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-bigpatent") # by default encoder-attention is `block_sparse` with num_random_blocks=3, block_size=64 model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent") # decoder attention type can't be changed & will be "original_full" # you can change `attention_type` (encoder only) to full attention like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent", attention_type="original_full") # you can change `block_size` & `num_random_blocks` like this: model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-bigpatent", block_size=16, num_random_blocks=2) text = "Replace me by any text you'd like." inputs = tokenizer(text, return_tensors='pt') prediction = model.generate(inputs) prediction = tokenizer.batch_decode(prediction) ``` ## Training Procedure This checkpoint is obtained after fine-tuning `BigBirdPegasusForConditionalGeneration` for summarization** on [big_patent](https://huggingface.co/datasets/big_patent) dataset. ## BibTeX entry and citation info ```tex @misc{zaheer2021big, title={Big Bird: Transformers for Longer Sequences}, author={Manzil Zaheer and Guru Guruganesh and Avinava Dubey and Joshua Ainslie and Chris Alberti and Santiago Ontanon and Philip Pham and Anirudh Ravula and Qifan Wang and Li Yang and Amr Ahmed}, year={2021}, eprint={2007.14062}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
google/bert2bert_L-24_wmt_de_en	google	2023-01-24T16:35:54Z	843	8	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "translation", "en", "de", "dataset:wmt14", "arxiv:1907.12461", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - en - de license: apache-2.0 datasets: - wmt14 tags: - translation --- # bert2bert_L-24_wmt_de_en EncoderDecoder model The model was introduced in [this paper](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn and first released in [this repository](https://tfhub.dev/google/bertseq2seq/bert24_de_en/1). The model is an encoder-decoder model that was initialized on the `bert-large` checkpoints for both the encoder and decoder and fine-tuned on German to English translation on the WMT dataset, which is linked above. Disclaimer: The model card has been written by the Hugging Face team. ## How to use You can use this model for translation, e.g. ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/bert2bert_L-24_wmt_de_en", pad_token="<pad>", eos_token="</s>", bos_token="<s>") model = AutoModelForSeq2SeqLM.from_pretrained("google/bert2bert_L-24_wmt_de_en") sentence = "Willst du einen Kaffee trinken gehen mit mir?" input_ids = tokenizer(sentence, return_tensors="pt", add_special_tokens=False).input_ids output_ids = model.generate(input_ids)[0] print(tokenizer.decode(output_ids, skip_special_tokens=True)) # should output # Want to drink a kaffee go with me? . ```
facebook/xglm-7.5B	facebook	2023-01-24T16:35:48Z	4,749	57	transformers	[ "transformers", "pytorch", "xglm", "text-generation", "multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu", "arxiv:2112.10668", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - multilingual - en - ru - zh - de - es - fr - ja - it - pt - el - ko - fi - id - tr - ar - vi - th - bg - ca - hi - et - bn - ta - ur - sw - te - eu - my - ht - qu license: mit thumbnail: https://huggingface.co/front/thumbnails/facebook.png inference: false --- # XGLM-7.5B XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\ (\Equal Contribution). The original implementation was released in [this repository](https://github.com/pytorch/fairseq/tree/main/examples/xglm). ## Training Data Statistics The training data statistics of XGLM-7.5B is shown in the table below. \| ISO-639-1\| family \| name \| # tokens \| ratio \| ratio w/ lowRes upsampling \| \|:--------\|:-----------------\|:------------------------\|-------------:\|------------:\|-------------:\| \| en \| Indo-European \| English \| 803526736124 \| 0.489906 \| 0.3259 \| \| ru \| Indo-European \| Russian \| 147791898098 \| 0.0901079 \| 0.0602 \| \| zh \| Sino-Tibetan \| Chinese \| 132770494630 \| 0.0809494 \| 0.0483 \| \| de \| Indo-European \| German \| 89223707856 \| 0.0543992 \| 0.0363 \| \| es \| Indo-European \| Spanish \| 87303083105 \| 0.0532282 \| 0.0353 \| \| fr \| Indo-European \| French \| 77419639775 \| 0.0472023 \| 0.0313 \| \| ja \| Japonic \| Japanese \| 66054364513 \| 0.040273 \| 0.0269 \| \| it \| Indo-European \| Italian \| 41930465338 \| 0.0255648 \| 0.0171 \| \| pt \| Indo-European \| Portuguese \| 36586032444 \| 0.0223063 \| 0.0297 \| \| el \| Indo-European \| Greek (modern) \| 28762166159 \| 0.0175361 \| 0.0233 \| \| ko \| Koreanic \| Korean \| 20002244535 \| 0.0121953 \| 0.0811 \| \| fi \| Uralic \| Finnish \| 16804309722 \| 0.0102455 \| 0.0681 \| \| id \| Austronesian \| Indonesian \| 15423541953 \| 0.00940365 \| 0.0125 \| \| tr \| Turkic \| Turkish \| 12413166065 \| 0.00756824 \| 0.0101 \| \| ar \| Afro-Asiatic \| Arabic \| 12248607345 \| 0.00746791 \| 0.0099 \| \| vi \| Austroasiatic \| Vietnamese \| 11199121869 \| 0.00682804 \| 0.0091 \| \| th \| Tai–Kadai \| Thai \| 10842172807 \| 0.00661041 \| 0.044 \| \| bg \| Indo-European \| Bulgarian \| 9703797869 \| 0.00591635 \| 0.0393 \| \| ca \| Indo-European \| Catalan \| 7075834775 \| 0.0043141 \| 0.0287 \| \| hi \| Indo-European \| Hindi \| 3448390110 \| 0.00210246 \| 0.014 \| \| et \| Uralic \| Estonian \| 3286873851 \| 0.00200399 \| 0.0133 \| \| bn \| Indo-European \| Bengali, Bangla \| 1627447450 \| 0.000992245 \| 0.0066 \| \| ta \| Dravidian \| Tamil \| 1476973397 \| 0.000900502 \| 0.006 \| \| ur \| Indo-European \| Urdu \| 1351891969 \| 0.000824241 \| 0.0055 \| \| sw \| Niger–Congo \| Swahili \| 907516139 \| 0.000553307 \| 0.0037 \| \| te \| Dravidian \| Telugu \| 689316485 \| 0.000420272 \| 0.0028 \| \| eu \| Language isolate \| Basque \| 105304423 \| 6.42035e-05 \| 0.0043 \| \| my \| Sino-Tibetan \| Burmese \| 101358331 \| 6.17976e-05 \| 0.003 \| \| ht \| Creole \| Haitian, Haitian Creole \| 86584697 \| 5.27902e-05 \| 0.0035 \| \| qu \| Quechuan \| Quechua \| 3236108 \| 1.97304e-06 \| 0.0001 \| ## Model card For intended usage of the model, please refer to the [model card](https://github.com/pytorch/fairseq/blob/main/examples/xglm/model_card.md) released by the XGLM-7.5B development team. ## Example (COPA) The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi. ```python import torch import torch.nn.functional as F from transformers import XGLMTokenizer, XGLMForCausalLM tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-7.5B") model = XGLMForCausalLM.from_pretrained("facebook/xglm-7.5B") data_samples = { 'en': [ { "premise": "I wanted to conserve energy.", "choice1": "I swept the floor in the unoccupied room.", "choice2": "I shut off the light in the unoccupied room.", "question": "effect", "label": "1" }, { "premise": "The flame on the candle went out.", "choice1": "I blew on the wick.", "choice2": "I put a match to the wick.", "question": "cause", "label": "0" } ], 'zh': [ { "premise": "我想节约能源。", "choice1": "我在空着的房间里扫了地板。", "choice2": "我把空房间里的灯关了。", "question": "effect", "label": "1" }, { "premise": "蜡烛上的火焰熄灭了。", "choice1": "我吹灭了灯芯。", "choice2": "我把一根火柴放在灯芯上。", "question": "cause", "label": "0" } ], 'hi': [ { "premise": "M te vle konsève enèji.", "choice1": "Mwen te fin baleye chanm lib la.", "choice2": "Mwen te femen limyè nan chanm lib la.", "question": "effect", "label": "1" }, { "premise": "Flam bouji a te etenn.", "choice1": "Mwen te soufle bouji a.", "choice2": "Mwen te limen mèch bouji a.", "question": "cause", "label": "0" } ] } def get_logprobs(prompt): inputs = tokenizer(prompt, return_tensors="pt") input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:] outputs = model(*inputs, labels=input_ids) logits = outputs.logits logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2)) return logprobs # Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task. # A return value of 0 indicates that the first alternative is more plausible, # while 1 indicates that the second alternative is more plausible. def COPA_eval(prompt, alternative1, alternative2): lprob1 = get_logprobs(prompt + "\n" + alternative1).sum() lprob2 = get_logprobs(prompt + "\n" + alternative2).sum() return 0 if lprob1 > lprob2 else 1 for lang in data_samples_long: for idx, example in enumerate(data_samples_long[lang]): predict = COPA_eval(example["premise"], example["choice1"], example["choice2"]) print(f'{lang}-{idx}', predict, example['label']) # en-0 1 1 # en-1 0 0 # zh-0 1 1 # zh-1 0 0 # hi-0 1 1 # hi-1 0 0 ```
facebook/xglm-564M	facebook	2023-01-24T16:35:45Z	15,028	51	transformers	[ "transformers", "pytorch", "tf", "jax", "xglm", "text-generation", "multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu", "arxiv:2112.10668", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - multilingual - en - ru - zh - de - es - fr - ja - it - pt - el - ko - fi - id - tr - ar - vi - th - bg - ca - hi - et - bn - ta - ur - sw - te - eu - my - ht - qu license: mit thumbnail: https://huggingface.co/front/thumbnails/facebook.png inference: false --- # XGLM-564M XGLM-564M is a multilingual autoregressive language model (with 564 million parameters) trained on a balanced corpus of a diverse set of 30 languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\ (\Equal Contribution). The original implementation was released in [this repository](https://github.com/pytorch/fairseq/tree/main/examples/xglm). ## Training Data Statistics The training data statistics of XGLM-564M is shown in the table below. \| ISO-639-1\| family \| name \| # tokens \| ratio \| ratio w/ lowRes upsampling \| \|:--------\|:-----------------\|:------------------------\|-------------:\|------------:\|-------------:\| \| en \| Indo-European \| English \| 803526736124 \| 0.489906 \| 0.3259 \| \| ru \| Indo-European \| Russian \| 147791898098 \| 0.0901079 \| 0.0602 \| \| zh \| Sino-Tibetan \| Chinese \| 132770494630 \| 0.0809494 \| 0.0483 \| \| de \| Indo-European \| German \| 89223707856 \| 0.0543992 \| 0.0363 \| \| es \| Indo-European \| Spanish \| 87303083105 \| 0.0532282 \| 0.0353 \| \| fr \| Indo-European \| French \| 77419639775 \| 0.0472023 \| 0.0313 \| \| ja \| Japonic \| Japanese \| 66054364513 \| 0.040273 \| 0.0269 \| \| it \| Indo-European \| Italian \| 41930465338 \| 0.0255648 \| 0.0171 \| \| pt \| Indo-European \| Portuguese \| 36586032444 \| 0.0223063 \| 0.0297 \| \| el \| Indo-European \| Greek (modern) \| 28762166159 \| 0.0175361 \| 0.0233 \| \| ko \| Koreanic \| Korean \| 20002244535 \| 0.0121953 \| 0.0811 \| \| fi \| Uralic \| Finnish \| 16804309722 \| 0.0102455 \| 0.0681 \| \| id \| Austronesian \| Indonesian \| 15423541953 \| 0.00940365 \| 0.0125 \| \| tr \| Turkic \| Turkish \| 12413166065 \| 0.00756824 \| 0.0101 \| \| ar \| Afro-Asiatic \| Arabic \| 12248607345 \| 0.00746791 \| 0.0099 \| \| vi \| Austroasiatic \| Vietnamese \| 11199121869 \| 0.00682804 \| 0.0091 \| \| th \| Tai–Kadai \| Thai \| 10842172807 \| 0.00661041 \| 0.044 \| \| bg \| Indo-European \| Bulgarian \| 9703797869 \| 0.00591635 \| 0.0393 \| \| ca \| Indo-European \| Catalan \| 7075834775 \| 0.0043141 \| 0.0287 \| \| hi \| Indo-European \| Hindi \| 3448390110 \| 0.00210246 \| 0.014 \| \| et \| Uralic \| Estonian \| 3286873851 \| 0.00200399 \| 0.0133 \| \| bn \| Indo-European \| Bengali, Bangla \| 1627447450 \| 0.000992245 \| 0.0066 \| \| ta \| Dravidian \| Tamil \| 1476973397 \| 0.000900502 \| 0.006 \| \| ur \| Indo-European \| Urdu \| 1351891969 \| 0.000824241 \| 0.0055 \| \| sw \| Niger–Congo \| Swahili \| 907516139 \| 0.000553307 \| 0.0037 \| \| te \| Dravidian \| Telugu \| 689316485 \| 0.000420272 \| 0.0028 \| \| eu \| Language isolate \| Basque \| 105304423 \| 6.42035e-05 \| 0.0043 \| \| my \| Sino-Tibetan \| Burmese \| 101358331 \| 6.17976e-05 \| 0.003 \| \| ht \| Creole \| Haitian, Haitian Creole \| 86584697 \| 5.27902e-05 \| 0.0035 \| \| qu \| Quechuan \| Quechua \| 3236108 \| 1.97304e-06 \| 0.0001 \| ## Model card For intended usage of the model, please refer to the [model card](https://github.com/pytorch/fairseq/blob/main/examples/xglm/model_card.md) released by the XGLM-564M development team. ## Example (COPA) The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi. ```python import torch import torch.nn.functional as F from transformers import XGLMTokenizer, XGLMForCausalLM tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M") model = XGLMForCausalLM.from_pretrained("facebook/xglm-564M") data_samples = { 'en': [ { "premise": "I wanted to conserve energy.", "choice1": "I swept the floor in the unoccupied room.", "choice2": "I shut off the light in the unoccupied room.", "question": "effect", "label": "1" }, { "premise": "The flame on the candle went out.", "choice1": "I blew on the wick.", "choice2": "I put a match to the wick.", "question": "cause", "label": "0" } ], 'zh': [ { "premise": "我想节约能源。", "choice1": "我在空着的房间里扫了地板。", "choice2": "我把空房间里的灯关了。", "question": "effect", "label": "1" }, { "premise": "蜡烛上的火焰熄灭了。", "choice1": "我吹灭了灯芯。", "choice2": "我把一根火柴放在灯芯上。", "question": "cause", "label": "0" } ], 'hi': [ { "premise": "M te vle konsève enèji.", "choice1": "Mwen te fin baleye chanm lib la.", "choice2": "Mwen te femen limyè nan chanm lib la.", "question": "effect", "label": "1" }, { "premise": "Flam bouji a te etenn.", "choice1": "Mwen te soufle bouji a.", "choice2": "Mwen te limen mèch bouji a.", "question": "cause", "label": "0" } ] } def get_logprobs(prompt): inputs = tokenizer(prompt, return_tensors="pt") input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:] outputs = model(*inputs, labels=input_ids) logits = outputs.logits logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2)) return logprobs # Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task. # A return value of 0 indicates that the first alternative is more plausible, # while 1 indicates that the second alternative is more plausible. def COPA_eval(prompt, alternative1, alternative2): lprob1 = get_logprobs(prompt + "\n" + alternative1).sum() lprob2 = get_logprobs(prompt + "\n" + alternative2).sum() return 0 if lprob1 > lprob2 else 1 for lang in data_samples_long: for idx, example in enumerate(data_samples_long[lang]): predict = COPA_eval(example["premise"], example["choice1"], example["choice2"]) print(f'{lang}-{idx}', predict, example['label']) # en-0 1 1 # en-1 0 0 # zh-0 1 1 # zh-1 0 0 # hi-0 1 1 # hi-1 0 0 ```
facebook/xglm-2.9B	facebook	2023-01-24T16:35:40Z	557	8	transformers	[ "transformers", "pytorch", "xglm", "text-generation", "multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu", "arxiv:2112.10668", "license:mit", "autotrain_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - multilingual - en - ru - zh - de - es - fr - ja - it - pt - el - ko - fi - id - tr - ar - vi - th - bg - ca - hi - et - bn - ta - ur - sw - te - eu - my - ht - qu license: mit thumbnail: https://huggingface.co/front/thumbnails/facebook.png inference: false --- # XGLM-2.9B XGLM-2.9B is a multilingual autoregressive language model (with 2.9 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\ (\Equal Contribution). The original implementation was released in [this repository](https://github.com/pytorch/fairseq/tree/main/examples/xglm). ## Training Data Statistics The training data statistics of XGLM-2.9B is shown in the table below. \| ISO-639-1\| family \| name \| # tokens \| ratio \| ratio w/ lowRes upsampling \| \|:--------\|:-----------------\|:------------------------\|-------------:\|------------:\|-------------:\| \| en \| Indo-European \| English \| 803526736124 \| 0.489906 \| 0.3259 \| \| ru \| Indo-European \| Russian \| 147791898098 \| 0.0901079 \| 0.0602 \| \| zh \| Sino-Tibetan \| Chinese \| 132770494630 \| 0.0809494 \| 0.0483 \| \| de \| Indo-European \| German \| 89223707856 \| 0.0543992 \| 0.0363 \| \| es \| Indo-European \| Spanish \| 87303083105 \| 0.0532282 \| 0.0353 \| \| fr \| Indo-European \| French \| 77419639775 \| 0.0472023 \| 0.0313 \| \| ja \| Japonic \| Japanese \| 66054364513 \| 0.040273 \| 0.0269 \| \| it \| Indo-European \| Italian \| 41930465338 \| 0.0255648 \| 0.0171 \| \| pt \| Indo-European \| Portuguese \| 36586032444 \| 0.0223063 \| 0.0297 \| \| el \| Indo-European \| Greek (modern) \| 28762166159 \| 0.0175361 \| 0.0233 \| \| ko \| Koreanic \| Korean \| 20002244535 \| 0.0121953 \| 0.0811 \| \| fi \| Uralic \| Finnish \| 16804309722 \| 0.0102455 \| 0.0681 \| \| id \| Austronesian \| Indonesian \| 15423541953 \| 0.00940365 \| 0.0125 \| \| tr \| Turkic \| Turkish \| 12413166065 \| 0.00756824 \| 0.0101 \| \| ar \| Afro-Asiatic \| Arabic \| 12248607345 \| 0.00746791 \| 0.0099 \| \| vi \| Austroasiatic \| Vietnamese \| 11199121869 \| 0.00682804 \| 0.0091 \| \| th \| Tai–Kadai \| Thai \| 10842172807 \| 0.00661041 \| 0.044 \| \| bg \| Indo-European \| Bulgarian \| 9703797869 \| 0.00591635 \| 0.0393 \| \| ca \| Indo-European \| Catalan \| 7075834775 \| 0.0043141 \| 0.0287 \| \| hi \| Indo-European \| Hindi \| 3448390110 \| 0.00210246 \| 0.014 \| \| et \| Uralic \| Estonian \| 3286873851 \| 0.00200399 \| 0.0133 \| \| bn \| Indo-European \| Bengali, Bangla \| 1627447450 \| 0.000992245 \| 0.0066 \| \| ta \| Dravidian \| Tamil \| 1476973397 \| 0.000900502 \| 0.006 \| \| ur \| Indo-European \| Urdu \| 1351891969 \| 0.000824241 \| 0.0055 \| \| sw \| Niger–Congo \| Swahili \| 907516139 \| 0.000553307 \| 0.0037 \| \| te \| Dravidian \| Telugu \| 689316485 \| 0.000420272 \| 0.0028 \| \| eu \| Language isolate \| Basque \| 105304423 \| 6.42035e-05 \| 0.0043 \| \| my \| Sino-Tibetan \| Burmese \| 101358331 \| 6.17976e-05 \| 0.003 \| \| ht \| Creole \| Haitian, Haitian Creole \| 86584697 \| 5.27902e-05 \| 0.0035 \| \| qu \| Quechuan \| Quechua \| 3236108 \| 1.97304e-06 \| 0.0001 \| ## Model card For intended usage of the model, please refer to the [model card](https://github.com/pytorch/fairseq/blob/main/examples/xglm/model_card.md) released by the XGLM-2.9B development team. ## Example (COPA) The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi. ```python import torch import torch.nn.functional as F from transformers import XGLMTokenizer, XGLMForCausalLM tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-2.9B") model = XGLMForCausalLM.from_pretrained("facebook/xglm-2.9B") data_samples = { 'en': [ { "premise": "I wanted to conserve energy.", "choice1": "I swept the floor in the unoccupied room.", "choice2": "I shut off the light in the unoccupied room.", "question": "effect", "label": "1" }, { "premise": "The flame on the candle went out.", "choice1": "I blew on the wick.", "choice2": "I put a match to the wick.", "question": "cause", "label": "0" } ], 'zh': [ { "premise": "我想节约能源。", "choice1": "我在空着的房间里扫了地板。", "choice2": "我把空房间里的灯关了。", "question": "effect", "label": "1" }, { "premise": "蜡烛上的火焰熄灭了。", "choice1": "我吹灭了灯芯。", "choice2": "我把一根火柴放在灯芯上。", "question": "cause", "label": "0" } ], 'hi': [ { "premise": "M te vle konsève enèji.", "choice1": "Mwen te fin baleye chanm lib la.", "choice2": "Mwen te femen limyè nan chanm lib la.", "question": "effect", "label": "1" }, { "premise": "Flam bouji a te etenn.", "choice1": "Mwen te soufle bouji a.", "choice2": "Mwen te limen mèch bouji a.", "question": "cause", "label": "0" } ] } def get_logprobs(prompt): inputs = tokenizer(prompt, return_tensors="pt") input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:] outputs = model(*inputs, labels=input_ids) logits = outputs.logits logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2)) return logprobs # Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task. # A return value of 0 indicates that the first alternative is more plausible, # while 1 indicates that the second alternative is more plausible. def COPA_eval(prompt, alternative1, alternative2): lprob1 = get_logprobs(prompt + "\n" + alternative1).sum() lprob2 = get_logprobs(prompt + "\n" + alternative2).sum() return 0 if lprob1 > lprob2 else 1 for lang in data_samples_long: for idx, example in enumerate(data_samples_long[lang]): predict = COPA_eval(example["premise"], example["choice1"], example["choice2"]) print(f'{lang}-{idx}', predict, example['label']) # en-0 1 1 # en-1 0 0 # zh-0 1 1 # zh-1 0 0 # hi-0 1 1 # hi-1 0 0 ```
facebook/wmt21-dense-24-wide-x-en	facebook	2023-01-24T16:35:35Z	26	13	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "translation", "wmt21", "multilingual", "ha", "is", "ja", "cs", "ru", "zh", "de", "en", "arxiv:2108.03265", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - multilingual - ha - is - ja - cs - ru - zh - de - en license: mit tags: - translation - wmt21 --- # WMT 21 X-En WMT 21 X-En is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository. The model can directly translate text from 7 languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de) to English. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example. To install `sentencepiece` run `pip install sentencepiece` Since the model was trained with domain tags, you should prepend them to the input as well. * "wmtdata newsdomain": Use for sentences in the news domain * "wmtdata otherdomain": Use for sentences in all other domain ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("facebook/wmt21-dense-24-wide-x-en") tokenizer = AutoTokenizer.from_pretrained("facebook/wmt21-dense-24-wide-x-en") # translate German to English tokenizer.src_lang = "de" inputs = tokenizer("wmtdata newsdomain Ein Modell für viele Sprachen", return_tensors="pt") generated_tokens = model.generate(inputs) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "A model for many languages" # translate Icelandic to English tokenizer.src_lang = "is" inputs = tokenizer("wmtdata newsdomain Ein fyrirmynd fyrir mörg tungumál", return_tensors="pt") generated_tokens = model.generate(inputs) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "One model for many languages" ``` See the [model hub](https://huggingface.co/models?filter=wmt21) to look for more fine-tuned versions. ## Languages covered English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de) ## BibTeX entry and citation info ``` @inproceedings{tran2021facebook title={Facebook AI’s WMT21 News Translation Task Submission}, author={Chau Tran and Shruti Bhosale and James Cross and Philipp Koehn and Sergey Edunov and Angela Fan}, booktitle={Proc. of WMT}, year={2021}, } ```
facebook/wmt21-dense-24-wide-en-x	facebook	2023-01-24T16:35:31Z	41	37	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "translation", "wmt21", "multilingual", "ha", "is", "ja", "cs", "ru", "zh", "de", "en", "arxiv:2108.03265", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - multilingual - ha - is - ja - cs - ru - zh - de - en license: mit tags: - translation - wmt21 --- # WMT 21 En-X WMT 21 En-X is a 4.7B multilingual encoder-decoder (seq-to-seq) model trained for one-to-many multilingual translation. It was introduced in this [paper](https://arxiv.org/abs/2108.03265) and first released in [this](https://github.com/pytorch/fairseq/tree/main/examples/wmt21) repository. The model can directly translate English text into 7 other languages: Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de). To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. Note: `M2M100Tokenizer` depends on `sentencepiece`, so make sure to install it before running the example. To install `sentencepiece` run `pip install sentencepiece` Since the model was trained with domain tags, you should prepend them to the input as well. * "wmtdata newsdomain": Use for sentences in the news domain * "wmtdata otherdomain": Use for sentences in all other domain ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("facebook/wmt21-dense-24-wide-en-x") tokenizer = AutoTokenizer.from_pretrained("facebook/wmt21-dense-24-wide-en-x") inputs = tokenizer("wmtdata newsdomain One model for many languages.", return_tensors="pt") # translate English to German generated_tokens = model.generate(inputs, forced_bos_token_id=tokenizer.get_lang_id("de")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Ein Modell für viele Sprachen." # translate English to Icelandic generated_tokens = model.generate(inputs, forced_bos_token_id=tokenizer.get_lang_id("is")) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Ein fyrirmynd fyrir mörg tungumál." ``` See the [model hub](https://huggingface.co/models?filter=wmt21) to look for more fine-tuned versions. ## Languages covered English (en), Hausa (ha), Icelandic (is), Japanese (ja), Czech (cs), Russian (ru), Chinese (zh), German (de) ## BibTeX entry and citation info ``` @inproceedings{tran2021facebook title={Facebook AI’s WMT21 News Translation Task Submission}, author={Chau Tran and Shruti Bhosale and James Cross and Philipp Koehn and Sergey Edunov and Angela Fan}, booktitle={Proc. of WMT}, year={2021}, } ```
facebook/wav2vec2-xls-r-2b-22-to-16	facebook	2023-01-24T16:35:01Z	32	14	transformers	[ "transformers", "pytorch", "speech-encoder-decoder", "automatic-speech-recognition", "speech", "xls_r", "xls_r_translation", "multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en", "dataset:common_voice", "dataset:multilingual_librispeech", "dataset:covost2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - multilingual - fr - de - es - ca - it - ru - zh - pt - fa - et - mn - nl - tr - ar - sv - lv - sl - ta - ja - id - cy - en datasets: - common_voice - multilingual_librispeech - covost2 tags: - speech - xls_r - automatic-speech-recognition - xls_r_translation pipeline_tag: automatic-speech-recognition license: apache-2.0 widget: - example_title: Swedish src: https://cdn-media.huggingface.co/speech_samples/cv_swedish_1.mp3 - example_title: Arabic src: https://cdn-media.huggingface.co/speech_samples/common_voice_ar_19058308.mp3 - example_title: Russian src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3 - example_title: German src: https://cdn-media.huggingface.co/speech_samples/common_voice_de_17284683.mp3 - example_title: French src: https://cdn-media.huggingface.co/speech_samples/common_voice_fr_17299386.mp3 - example_title: Indonesian src: https://cdn-media.huggingface.co/speech_samples/common_voice_id_19051309.mp3 - example_title: Italian src: https://cdn-media.huggingface.co/speech_samples/common_voice_it_17415776.mp3 - example_title: Japanese src: https://cdn-media.huggingface.co/speech_samples/common_voice_ja_19482488.mp3 - example_title: Mongolian src: https://cdn-media.huggingface.co/speech_samples/common_voice_mn_18565396.mp3 - example_title: Dutch src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3 - example_title: Russian src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3 - example_title: Turkish src: https://cdn-media.huggingface.co/speech_samples/common_voice_tr_17341280.mp3 - example_title: Catalan src: https://cdn-media.huggingface.co/speech_samples/common_voice_ca_17367522.mp3 - example_title: English src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3 - example_title: Dutch src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3 --- # Wav2Vec2-XLS-R-2B-22-16 (XLS-R-Any-to-Any) Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation. ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png) This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. The encoder was warm-started from the [`facebook/wav2vec2-xls-r-2b`](https://huggingface.co/facebook/wav2vec2-xls-r-2b) checkpoint and the decoder from the [`facebook/mbart-large-50`](https://huggingface.co/facebook/mbart-large-50) checkpoint. Consequently, the encoder-decoder model was fine-tuned on `{input_lang}` -> `{output_lang}` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2). The model can translate from the following spoken languages `{input_lang}` to the following written languages `{output_lang}`: `{input_lang}` -> `{output_lang}` with `{input_lang}` one of: {`en`, `fr`, `de`, `es`, `ca`, `it`, `ru`, `zh-CN`, `pt`, `fa`, `et`, `mn`, `nl`, `tr`, `ar`, `sv-SE`, `lv`, `sl`, `ta`, `ja`, `id`, `cy`} and `{output_lang}`: {`en`, `de`, `tr`, `fa`, `sv-SE`, `mn`, `zh-CN`, `cy`, `ca`, `sl`, `et`, `id`, `ar`, `ta`, `lv`, `ja`} ## Usage ### Demo The model can be tested on [this space](https://huggingface.co/spaces/facebook/XLS-R-2B-22-16). You can select the target language, record some audio in any of the above mentioned input languages, and then sit back and see how well the checkpoint can translate the input. ### Example As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline. By default, the checkpoint will translate spoken English to written German. To change the written target language, you need to pass the correct `forced_bos_token_id` to `generate(...)` to condition the decoder on the correct target language. To select the correct `forced_bos_token_id` given your choosen language id, please make use of the following mapping: ```python MAPPING = { "en": 250004, "de": 250003, "tr": 250023, "fa": 250029, "sv": 250042, "mn": 250037, "zh": 250025, "cy": 250007, "ca": 250005, "sl": 250052, "et": 250006, "id": 250032, "ar": 250001, "ta": 250044, "lv": 250017, "ja": 250012, } ``` As an example, if you would like to translate to Swedish, you can do the following: ```python from datasets import load_dataset from transformers import pipeline # select correct `forced_bos_token_id` forced_bos_token_id = MAPPING["sv"] # replace following lines to load an audio file of your choice librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") audio_file = librispeech_en[0]["file"] asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-2b-22-to-16", feature_extractor="facebook/wav2vec2-xls-r-2b-22-to-16") translation = asr(audio_file, forced_bos_token_id=forced_bos_token_id) ``` or step-by-step as follows: ```python import torch from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel from datasets import load_dataset model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-2b-22-to-16") processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-2b-22-to-16") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # select correct `forced_bos_token_id` forced_bos_token_id = MAPPING["sv"] inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["array"]["sampling_rate"], return_tensors="pt") generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"], forced_bos_token_id=forced_bos_token) transcription = processor.batch_decode(generated_ids) ``` ## More XLS-R models for `{lang}` -> `en` Speech Translation - [Wav2Vec2-XLS-R-300M-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-300m-en-to-15) - [Wav2Vec2-XLS-R-1B-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-1b-en-to-15) - [Wav2Vec2-XLS-R-2B-EN-15](https://huggingface.co/facebook/wav2vec2-xls-r-2b-en-to-15) - [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)
facebook/wav2vec2-xls-r-2b-21-to-en	facebook	2023-01-24T16:34:58Z	24	5	transformers	[ "transformers", "pytorch", "speech-encoder-decoder", "automatic-speech-recognition", "speech", "xls_r", "xls_r_translation", "multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en", "dataset:common_voice", "dataset:multilingual_librispeech", "dataset:covost2", "arxiv:2111.09296", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - multilingual - fr - de - es - ca - it - ru - zh - pt - fa - et - mn - nl - tr - ar - sv - lv - sl - ta - ja - id - cy - en datasets: - common_voice - multilingual_librispeech - covost2 tags: - speech - xls_r - automatic-speech-recognition - xls_r_translation pipeline_tag: automatic-speech-recognition license: apache-2.0 widget: - example_title: Swedish src: https://cdn-media.huggingface.co/speech_samples/cv_swedish_1.mp3 - example_title: Arabic src: https://cdn-media.huggingface.co/speech_samples/common_voice_ar_19058308.mp3 - example_title: Russian src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3 - example_title: German src: https://cdn-media.huggingface.co/speech_samples/common_voice_de_17284683.mp3 - example_title: French src: https://cdn-media.huggingface.co/speech_samples/common_voice_fr_17299386.mp3 - example_title: Indonesian src: https://cdn-media.huggingface.co/speech_samples/common_voice_id_19051309.mp3 - example_title: Italian src: https://cdn-media.huggingface.co/speech_samples/common_voice_it_17415776.mp3 - example_title: Japanese src: https://cdn-media.huggingface.co/speech_samples/common_voice_ja_19482488.mp3 - example_title: Mongolian src: https://cdn-media.huggingface.co/speech_samples/common_voice_mn_18565396.mp3 - example_title: Dutch src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3 - example_title: Russian src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3 - example_title: Turkish src: https://cdn-media.huggingface.co/speech_samples/common_voice_tr_17341280.mp3 - example_title: Catalan src: https://cdn-media.huggingface.co/speech_samples/common_voice_ca_17367522.mp3 - example_title: English src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3 - example_title: Dutch src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3 --- # Wav2Vec2-XLS-R-2b-21-EN Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation. ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png) This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. The encoder was warm-started from the [`facebook/wav2vec2-xls-r-2b`](https://huggingface.co/facebook/wav2vec2-xls-r-2b) checkpoint and the decoder from the [`facebook/mbart-large-50`](https://huggingface.co/facebook/mbart-large-50) checkpoint. Consequently, the encoder-decoder model was fine-tuned on 21 `{lang}` -> `en` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2). The model can translate from the following spoken languages `{lang}` -> `en` (English): {`fr`, `de`, `es`, `ca`, `it`, `ru`, `zh-CN`, `pt`, `fa`, `et`, `mn`, `nl`, `tr`, `ar`, `sv-SE`, `lv`, `sl`, `ta`, `ja`, `id`, `cy`} -> `en` For more information, please refer to Section 5.1.2 of the [official XLS-R paper](https://arxiv.org/abs/2111.09296). ## Usage ### Demo The model can be tested directly on the speech recognition widget on this model card! Simple record some audio in one of the possible spoken languages or pick an example audio file to see how well the checkpoint can translate the input. ### Example As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline ```python from datasets import load_dataset from transformers import pipeline # replace following lines to load an audio file of your choice librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") audio_file = librispeech_en[0]["file"] asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-2b-21-to-en", feature_extractor="facebook/wav2vec2-xls-r-2b-21-to-en") translation = asr(audio_file) ``` or step-by-step as follows: ```python import torch from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel from datasets import load_dataset model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-2b-21-to-en") processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-2b-21-to-en") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["array"]["sampling_rate"], return_tensors="pt") generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) transcription = processor.batch_decode(generated_ids) ``` ## Results `{lang}` -> `en` See the row of XLS-R (2B) for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model. ![results image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/X-%3EEnglish.png) ## More XLS-R models for `{lang}` -> `en` Speech Translation - [Wav2Vec2-XLS-R-300M-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-300m-21-to-en) - [Wav2Vec2-XLS-R-1B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-1b-21-to-en) - [Wav2Vec2-XLS-R-2B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-2b-21-to-en) - [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)
facebook/wav2vec2-xls-r-1b-21-to-en	facebook	2023-01-24T16:34:50Z	381	3	transformers	[ "transformers", "pytorch", "speech-encoder-decoder", "automatic-speech-recognition", "speech", "xls_r", "xls_r_translation", "multilingual", "fr", "de", "es", "ca", "it", "ru", "zh", "pt", "fa", "et", "mn", "nl", "tr", "ar", "sv", "lv", "sl", "ta", "ja", "id", "cy", "en", "dataset:common_voice", "dataset:multilingual_librispeech", "dataset:covost2", "arxiv:2111.09296", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - multilingual - fr - de - es - ca - it - ru - zh - pt - fa - et - mn - nl - tr - ar - sv - lv - sl - ta - ja - id - cy - en datasets: - common_voice - multilingual_librispeech - covost2 tags: - speech - xls_r - automatic-speech-recognition - xls_r_translation pipeline_tag: automatic-speech-recognition license: apache-2.0 widget: - example_title: Swedish src: https://cdn-media.huggingface.co/speech_samples/cv_swedish_1.mp3 - example_title: Arabic src: https://cdn-media.huggingface.co/speech_samples/common_voice_ar_19058308.mp3 - example_title: Russian src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3 - example_title: German src: https://cdn-media.huggingface.co/speech_samples/common_voice_de_17284683.mp3 - example_title: French src: https://cdn-media.huggingface.co/speech_samples/common_voice_fr_17299386.mp3 - example_title: Indonesian src: https://cdn-media.huggingface.co/speech_samples/common_voice_id_19051309.mp3 - example_title: Italian src: https://cdn-media.huggingface.co/speech_samples/common_voice_it_17415776.mp3 - example_title: Japanese src: https://cdn-media.huggingface.co/speech_samples/common_voice_ja_19482488.mp3 - example_title: Mongolian src: https://cdn-media.huggingface.co/speech_samples/common_voice_mn_18565396.mp3 - example_title: Dutch src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3 - example_title: Russian src: https://cdn-media.huggingface.co/speech_samples/common_voice_ru_18849022.mp3 - example_title: Turkish src: https://cdn-media.huggingface.co/speech_samples/common_voice_tr_17341280.mp3 - example_title: Catalan src: https://cdn-media.huggingface.co/speech_samples/common_voice_ca_17367522.mp3 - example_title: English src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3 - example_title: Dutch src: https://cdn-media.huggingface.co/speech_samples/common_voice_nl_17691471.mp3 --- # Wav2Vec2-XLS-R-2b-21-EN Facebook's Wav2Vec2 XLS-R fine-tuned for Speech Translation. ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png) This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. The encoder was warm-started from the [`facebook/wav2vec2-xls-r-1b`](https://huggingface.co/facebook/wav2vec2-xls-r-1b) checkpoint and the decoder from the [`facebook/mbart-large-50`](https://huggingface.co/facebook/mbart-large-50) checkpoint. Consequently, the encoder-decoder model was fine-tuned on 21 `{lang}` -> `en` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2). The model can translate from the following spoken languages `{lang}` -> `en` (English): {`fr`, `de`, `es`, `ca`, `it`, `ru`, `zh-CN`, `pt`, `fa`, `et`, `mn`, `nl`, `tr`, `ar`, `sv-SE`, `lv`, `sl`, `ta`, `ja`, `id`, `cy`} -> `en` For more information, please refer to Section 5.1.2 of the [official XLS-R paper](https://arxiv.org/abs/2111.09296). ## Usage ### Demo The model can be tested directly on the speech recognition widget on this model card! Simple record some audio in one of the possible spoken languages or pick an example audio file to see how well the checkpoint can translate the input. ### Example As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline ```python from datasets import load_dataset from transformers import pipeline # replace following lines to load an audio file of your choice librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") audio_file = librispeech_en[0]["file"] asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-1b-21-to-en", feature_extractor="facebook/wav2vec2-xls-r-1b-21-to-en") translation = asr(audio_file) ``` or step-by-step as follows: ```python import torch from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel from datasets import load_dataset model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-1b-21-to-en") processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-1b-21-to-en") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["array"]["sampling_rate"], return_tensors="pt") generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) transcription = processor.batch_decode(generated_ids) ``` ## Results `{lang}` -> `en` See the row of XLS-R (1B) for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model. ![results image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/X-%3EEnglish.png) ## More XLS-R models for `{lang}` -> `en` Speech Translation - [Wav2Vec2-XLS-R-300M-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-300m-21-to-en) - [Wav2Vec2-XLS-R-1B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-1b-21-to-en) - [Wav2Vec2-XLS-R-2B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-2b-21-to-en) - [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)
facebook/s2t-wav2vec2-large-en-tr	facebook	2023-01-24T16:32:38Z	46	3	transformers	[ "transformers", "pytorch", "speech-encoder-decoder", "automatic-speech-recognition", "audio", "speech-translation", "speech2text2", "en", "tr", "dataset:covost2", "dataset:librispeech_asr", "arxiv:2104.06678", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - en - tr datasets: - covost2 - librispeech_asr tags: - audio - speech-translation - automatic-speech-recognition - speech2text2 license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Common Voice 1 src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99989.mp3 - example_title: Common Voice 2 src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99986.mp3 - example_title: Common Voice 3 src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99987.mp3 --- # S2T2-Wav2Vec2-CoVoST2-EN-TR-ST `s2t-wav2vec2-large-en-tr` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST). The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in [Fairseq](https://github.com/pytorch/fairseq/blob/6f847c8654d56b4d1b1fbacec027f47419426ddb/fairseq/models/wav2vec/wav2vec2_asr.py#L266). ## Model description S2T2 is a transformer-based seq2seq (speech encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a pretrained [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html) as the encoder and a transformer-based decoder. The model is trained with standard autoregressive cross-entropy loss and generates the translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Turkish text translation. See the [model hub](https://huggingface.co/models?filter=speech2text2) to look for other S2T2 checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline ```python from datasets import load_dataset from transformers import pipeline librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-tr", feature_extractor="facebook/s2t-wav2vec2-large-en-tr") translation = asr(librispeech_en[0]["file"]) ``` or step-by-step as follows: ```python import torch from transformers import Speech2Text2Processor, SpeechEncoderDecoder from datasets import load_dataset import soundfile as sf model = SpeechEncoderDecoder.from_pretrained("facebook/s2t-wav2vec2-large-en-tr") processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-tr") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.map(map_to_array) inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt") generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) transcription = processor.batch_decode(generated_ids) ``` ## Evaluation results CoVoST-V2 test results for en-tr (BLEU score): 17.5 For more information, please have a look at the [official paper](https://arxiv.org/pdf/2104.06678.pdf) - especially row 10 of Table 2. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2104-06678, author = {Changhan Wang and Anne Wu and Juan Miguel Pino and Alexei Baevski and Michael Auli and Alexis Conneau}, title = {Large-Scale Self- and Semi-Supervised Learning for Speech Translation}, journal = {CoRR}, volume = {abs/2104.06678}, year = {2021}, url = {https://arxiv.org/abs/2104.06678}, archivePrefix = {arXiv}, eprint = {2104.06678}, timestamp = {Thu, 12 Aug 2021 15:37:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-06678.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
facebook/s2t-wav2vec2-large-en-ca	facebook	2023-01-24T16:32:32Z	6	2	transformers	[ "transformers", "pytorch", "speech-encoder-decoder", "automatic-speech-recognition", "audio", "speech-translation", "speech2text2", "en", "ca", "dataset:covost2", "dataset:librispeech_asr", "arxiv:2104.06678", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - en - ca datasets: - covost2 - librispeech_asr tags: - audio - speech-translation - automatic-speech-recognition - speech2text2 license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Common Voice 1 src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_18301577.mp3 - example_title: Common Voice 2 src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_99989.mp3 - example_title: Common Voice 3 src: https://cdn-media.huggingface.co/speech_samples/common_voice_en_9999.mp3 --- # S2T2-Wav2Vec2-CoVoST2-EN-CA-ST `s2t-wav2vec2-large-en-ca` is a Speech to Text Transformer model trained for end-to-end Speech Translation (ST). The S2T2 model was proposed in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/pdf/2104.06678.pdf) and officially released in [Fairseq](https://github.com/pytorch/fairseq/blob/6f847c8654d56b4d1b1fbacec027f47419426ddb/fairseq/models/wav2vec/wav2vec2_asr.py#L266). ## Model description S2T2 is a transformer-based seq2seq (speech encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a pretrained [Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html) as the encoder and a transformer-based decoder. The model is trained with standard autoregressive cross-entropy loss and generates the translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Catalan text translation. See the [model hub](https://huggingface.co/models?filter=speech2text2) to look for other S2T2 checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. You can use the model directly via the ASR pipeline ```python from datasets import load_dataset from transformers import pipeline librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-ca", feature_extractor="facebook/s2t-wav2vec2-large-en-ca") translation = asr(librispeech_en[0]["file"]) ``` or step-by-step as follows: ```python import torch from transformers import Speech2Text2Processor, SpeechEncoderDecoder from datasets import load_dataset import soundfile as sf model = SpeechEncoderDecoder.from_pretrained("facebook/s2t-wav2vec2-large-en-ca") processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-ca") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.map(map_to_array) inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt") generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) transcription = processor.batch_decode(generated_ids) ``` ## Evaluation results CoVoST-V2 test results for en-ca (BLEU score): 34.1 For more information, please have a look at the [official paper](https://arxiv.org/pdf/2104.06678.pdf) - especially row 10 of Table 2. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2104-06678, author = {Changhan Wang and Anne Wu and Juan Miguel Pino and Alexei Baevski and Michael Auli and Alexis Conneau}, title = {Large-Scale Self- and Semi-Supervised Learning for Speech Translation}, journal = {CoRR}, volume = {abs/2104.06678}, year = {2021}, url = {https://arxiv.org/abs/2104.06678}, archivePrefix = {arXiv}, eprint = {2104.06678}, timestamp = {Thu, 12 Aug 2021 15:37:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-06678.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
facebook/s2t-small-mustc-en-ru-st	facebook	2023-01-24T16:32:26Z	12	1	transformers	[ "transformers", "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "audio", "speech-translation", "en", "ru", "dataset:mustc", "arxiv:2010.05171", "arxiv:1904.08779", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - en - ru datasets: - mustc tags: - audio - speech-translation - automatic-speech-recognition license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # S2T-SMALL-MUSTC-EN-RU-ST `s2t-small-mustc-en-ru-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Russian text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example. You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-ru-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-ru-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=16_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-mustc-en-ru-st is trained on English-Russian subset of [MuST-C](https://ict.fbk.eu/must-c/). MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 8,000. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results MuST-C test results for en-ru (BLEU score): 15.3 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
facebook/s2t-small-mustc-en-ro-st	facebook	2023-01-24T16:32:22Z	10	0	transformers	[ "transformers", "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "audio", "speech-translation", "en", "ro", "dataset:mustc", "arxiv:2010.05171", "arxiv:1904.08779", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - en - ro datasets: - mustc tags: - audio - speech-translation - automatic-speech-recognition license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # S2T-SMALL-MUSTC-EN-RO-ST `s2t-small-mustc-en-ro-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Romanian text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example. You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-ro-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-ro-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=16_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-mustc-en-ro-st is trained on English-Romanian subset of [MuST-C](https://ict.fbk.eu/must-c/). MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 8,000. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results MuST-C test results for en-ro (BLEU score): 21.9 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
facebook/s2t-small-mustc-en-pt-st	facebook	2023-01-24T16:32:19Z	6	2	transformers	[ "transformers", "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "audio", "speech-translation", "en", "pt", "dataset:mustc", "arxiv:2010.05171", "arxiv:1904.08779", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - en - pt datasets: - mustc tags: - audio - speech-translation - automatic-speech-recognition license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # S2T-SMALL-MUSTC-EN-PT-ST `s2t-small-mustc-en-pt-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to Portuguese text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example. You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-pt-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-pt-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=16_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-mustc-en-pt-st is trained on English-Portuguese subset of [MuST-C](https://ict.fbk.eu/must-c/). MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 8,000. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results MuST-C test results for en-pt (BLEU score): 28.1 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
facebook/s2t-small-covost2-es-en-st	facebook	2023-01-24T16:31:55Z	4	0	transformers	[ "transformers", "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "audio", "speech-translation", "es", "en", "dataset:covost2", "arxiv:2010.05171", "arxiv:1912.06670", "arxiv:1904.08779", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - es - en datasets: - covost2 tags: - audio - speech-translation - automatic-speech-recognition license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # S2T-SMALL-COVOST2-ES-EN-ST `s2t-small-covost2-es-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end Spanish speech to English text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example. You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-es-en-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-es-en-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-covost2-es-en-st is trained on Spanish-English subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using character based SentencePiece vocab. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results CoVOST2 test results for es-en (BLEU score): 22.31 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
facebook/s2t-small-covost2-ca-en-st	facebook	2023-01-24T16:31:36Z	10	0	transformers	[ "transformers", "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "audio", "speech-translation", "ca", "en", "dataset:covost2", "arxiv:2010.05171", "arxiv:1912.06670", "arxiv:1904.08779", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - ca - en datasets: - covost2 tags: - audio - speech-translation - automatic-speech-recognition license: mit pipeline_tag: automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac --- # S2T-SMALL-COVOST2-CA-EN-ST `s2t-small-covost2-ca-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end Catalan speech to English text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example. You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-ca-en-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-ca-en-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-small-covost2-ca-en-st is trained on Catalan-English subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using character based SentencePiece vocab. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR. ## Evaluation results CoVOST2 test results for ca-en (BLEU score): 17.85 ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
facebook/s2t-medium-mustc-multilingual-st	facebook	2023-01-24T16:31:33Z	6,629	6	transformers	[ "transformers", "pytorch", "tf", "speech_to_text", "automatic-speech-recognition", "audio", "speech-translation", "en", "de", "nl", "es", "fr", "it", "pt", "ro", "ru", "dataset:mustc", "arxiv:2010.05171", "arxiv:1904.08779", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - en - de - nl - es - fr - it - pt - ro - ru datasets: - mustc tags: - audio - speech-translation - automatic-speech-recognition pipeline_tag: automatic-speech-recognition license: mit --- # S2T-MEDIUM-MUSTC-MULTILINGUAL-ST `s2t-medium-mustc-multilingual-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Multilingual Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text) ## Model description S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. ## Intended uses & limitations This model can be used for end-to-end English speech to French text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. ### How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. For multilingual speech translation models, `eos_token_id` is used as the `decoder_start_token_id` and the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate()` method. The following example shows how to transate English speech to French and German text using the `facebook/s2t-medium-mustc-multilingual-st` checkpoint. Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example. You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-mustc-multilingual-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.map(map_to_array) inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt") # translate English Speech To French Text generated_ids = model.generate( input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"], forced_bos_token_id=processor.tokenizer.lang_code_to_id["fr"] ) translation_fr = processor.batch_decode(generated_ids) # translate English Speech To German Text generated_ids = model.generate( input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"], forced_bos_token_id=processor.tokenizer.lang_code_to_id["de"] ) translation_de = processor.batch_decode(generated_ids, skip_special_tokens=True) ``` ## Training data The s2t-medium-mustc-multilingual-st is trained on [MuST-C](https://ict.fbk.eu/must-c/). MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems for speech translation from English into several languages. For each target language, MuST-C comprises several hundred hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. ## Training procedure ### Preprocessing The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example. The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 10,000. ### Training The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779). The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for multilingual ASR. For multilingual models, target language ID token is used as target BOS. ## Evaluation results MuST-C test results (BLEU score): \| En-De \| En-Nl \| En-Es \| En-Fr \| En-It \| En-Pt \| En-Ro \| En-Ru \| \|:-----:\|:-----:\|:-----:\|:-----:\|:-----:\|:-----:\|:-----:\|:-----:\| \| 24.5 \| 28.6 \| 28.2 \| 34.9 \| 24.6 \| 31.1 \| 23.8 \| 16.0 \| ### BibTeX entry and citation info ```bibtex @inproceedings{wang2020fairseqs2t, title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq}, author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino}, booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations}, year = {2020}, } ```
facebook/blenderbot-90M	facebook	2023-01-24T16:29:11Z	4,767	3	transformers	[ "transformers", "pytorch", "blenderbot-small", "text2text-generation", "convAI", "conversational", "facebook", "en", "dataset:blended_skill_talk", "arxiv:1907.06616", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - en thumbnail: tags: - convAI - conversational - facebook license: apache-2.0 datasets: - blended_skill_talk metrics: - perplexity --- # 🚨🚨IMPORTANT🚨🚨 This model is deprecated! Please use the identical model https://huggingface.co/facebook/blenderbot_small-90M instead ## Model description + Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616) + [Original PARLAI Code](https://parl.ai/projects/recipes/) ### Abstract Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
facebook/bart-large-xsum	facebook	2023-01-24T16:28:59Z	11,853	35	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "bart", "text2text-generation", "summarization", "en", "arxiv:1910.13461", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- tags: - summarization language: - en license: mit model-index: - name: facebook/bart-large-xsum results: - task: type: summarization name: Summarization dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: test metrics: - name: ROUGE-1 type: rouge value: 25.2697 verified: true - name: ROUGE-2 type: rouge value: 7.6638 verified: true - name: ROUGE-L type: rouge value: 17.1808 verified: true - name: ROUGE-LSUM type: rouge value: 21.7933 verified: true - name: loss type: loss value: 3.5042972564697266 verified: true - name: gen_len type: gen_len value: 27.4462 verified: true - task: type: summarization name: Summarization dataset: name: xsum type: xsum config: default split: test metrics: - name: ROUGE-1 type: rouge value: 45.4525 verified: true - name: ROUGE-2 type: rouge value: 22.3455 verified: true - name: ROUGE-L type: rouge value: 37.2302 verified: true - name: ROUGE-LSUM type: rouge value: 37.2323 verified: true - name: loss type: loss value: 2.3128726482391357 verified: true - name: gen_len type: gen_len value: 25.5435 verified: true - task: type: summarization name: Summarization dataset: name: samsum type: samsum config: samsum split: train metrics: - name: ROUGE-1 type: rouge value: 24.7852 verified: true - name: ROUGE-2 type: rouge value: 5.2533 verified: true - name: ROUGE-L type: rouge value: 18.6792 verified: true - name: ROUGE-LSUM type: rouge value: 20.629 verified: true - name: loss type: loss value: 3.746837854385376 verified: true - name: gen_len type: gen_len value: 23.1206 verified: true - task: type: summarization name: Summarization dataset: name: samsum type: samsum config: samsum split: test metrics: - name: ROUGE-1 type: rouge value: 24.9158 verified: true - name: ROUGE-2 type: rouge value: 5.5837 verified: true - name: ROUGE-L type: rouge value: 18.8935 verified: true - name: ROUGE-LSUM type: rouge value: 20.76 verified: true - name: loss type: loss value: 3.775235891342163 verified: true - name: gen_len type: gen_len value: 23.0928 verified: true --- ### Bart model finetuned on xsum docs: https://huggingface.co/transformers/model_doc/bart.html finetuning: examples/seq2seq/ (as of Aug 20, 2020) Metrics: ROUGE > 22 on xsum. variants: search for distilbart paper: https://arxiv.org/abs/1910.13461
allenai/wmt19-de-en-6-6-base	allenai	2023-01-24T16:28:48Z	30	0	transformers	[ "transformers", "pytorch", "fsmt", "text2text-generation", "translation", "wmt19", "allenai", "de", "en", "dataset:wmt19", "arxiv:2006.10369", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - de - en thumbnail: tags: - translation - wmt19 - allenai license: apache-2.0 datasets: - wmt19 metrics: - bleu --- # FSMT ## Model description This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for de-en. For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). 2 models are available: * [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big) * [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt19-de-en-6-6-base" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Maschinelles Lernen ist großartig, nicht wahr?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Machine learning is great, isn't it? ``` #### Limitations and bias ## Training data Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369). ## Eval results Here are the BLEU scores: model \| transformers -------\|--------- wmt19-de-en-6-6-base \| 38.37 The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt19-de-en-6-6-base $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ``` @misc{kasai2020deep, title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}, author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}, year={2020}, eprint={2006.10369}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
allenai/wmt16-en-de-dist-6-1	allenai	2023-01-24T16:28:45Z	28	1	transformers	[ "transformers", "pytorch", "fsmt", "text2text-generation", "translation", "wmt16", "allenai", "en", "de", "dataset:wmt16", "arxiv:2006.10369", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - en - de thumbnail: tags: - translation - wmt16 - allenai license: apache-2.0 datasets: - wmt16 metrics: - bleu --- # FSMT ## Model description This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de. For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). All 3 models are available: * [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1) * [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1) * [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt16-en-de-dist-6-1" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Maschinelles Lernen ist großartig, nicht wahr? ``` #### Limitations and bias ## Training data Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369). ## Eval results Here are the BLEU scores: model \| fairseq \| transformers -------\|---------\|---------- wmt16-en-de-dist-6-1 \| 27.4 \| 27.11 The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs. The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=en-de export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-dist-6-1 $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` ## Data Sources - [training, etc.](http://www.statmt.org/wmt16/) - [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372) ### BibTeX entry and citation info ``` @misc{kasai2020deep, title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}, author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}, year={2020}, eprint={2006.10369}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
allenai/wmt16-en-de-dist-12-1	allenai	2023-01-24T16:28:42Z	44	1	transformers	[ "transformers", "pytorch", "fsmt", "text2text-generation", "translation", "wmt16", "allenai", "en", "de", "dataset:wmt16", "arxiv:2006.10369", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - en - de thumbnail: tags: - translation - wmt16 - allenai license: apache-2.0 datasets: - wmt16 metrics: - bleu --- # FSMT ## Model description This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de. For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). All 3 models are available: * [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1) * [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1) * [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt16-en-de-dist-12-1" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Maschinelles Lernen ist großartig, nicht wahr? ``` #### Limitations and bias ## Training data Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369). ## Eval results Here are the BLEU scores: model \| fairseq \| transformers -------\|---------\|---------- wmt16-en-de-dist-12-1 \| 28.3 \| 27.52 The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs. The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=en-de export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-dist-12-1 $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` ## Data Sources - [training, etc.](http://www.statmt.org/wmt16/) - [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372) ### BibTeX entry and citation info ``` @misc{kasai2020deep, title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}, author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}, year={2020}, eprint={2006.10369}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
allenai/wmt16-en-de-12-1	allenai	2023-01-24T16:28:39Z	22	1	transformers	[ "transformers", "pytorch", "fsmt", "text2text-generation", "translation", "wmt16", "allenai", "en", "de", "dataset:wmt16", "arxiv:2006.10369", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - en - de thumbnail: tags: - translation - wmt16 - allenai license: apache-2.0 datasets: - wmt16 metrics: - bleu --- # FSMT ## Model description This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de. For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). All 3 models are available: * [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1) * [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1) * [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt16-en-de-12-1" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # Maschinelles Lernen ist großartig, nicht wahr? ``` #### Limitations and bias ## Training data Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369). ## Eval results Here are the BLEU scores: model \| fairseq \| transformers -------\|---------\|---------- wmt16-en-de-12-1 \| 26.9 \| 25.75 The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs. The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR=en-de export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=5 mkdir -p $DATA_DIR sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-12-1 $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` ## Data Sources - [training, etc.](http://www.statmt.org/wmt16/) - [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372) ### BibTeX entry and citation info ``` @misc{kasai2020deep, title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}, author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}, year={2020}, eprint={2006.10369}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
allenai/unifiedqa-v2-t5-small-1251000	allenai	2023-01-24T16:28:33Z	12	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- # Further details: https://github.com/allenai/unifiedqa
allenai/unifiedqa-v2-t5-large-1363200	allenai	2023-01-24T16:28:30Z	321	4	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- # Further details: https://github.com/allenai/unifiedqa
allenai/unifiedqa-v2-t5-large-1251000	allenai	2023-01-24T16:28:27Z	12	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- # Further details: https://github.com/allenai/unifiedqa
allenai/unifiedqa-v2-t5-base-1363200	allenai	2023-01-24T16:28:24Z	308	2	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- # Further details: https://github.com/allenai/unifiedqa
allenai/unifiedqa-v2-t5-base-1251000	allenai	2023-01-24T16:28:21Z	90	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- # Further details: https://github.com/allenai/unifiedqa
allenai/unifiedqa-v2-t5-3b-1251000	allenai	2023-01-24T16:28:15Z	67	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- # Further details: https://github.com/allenai/unifiedqa
allenai/t5-small-squad2-question-generation	allenai	2023-01-24T16:27:47Z	501	43	transformers	[ "transformers", "pytorch", "jax", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- A simple question-generation model built based on SQuAD 2.0 dataset. Example use: ```python from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer model_name = "allenai/t5-small-squad2-question-generation" tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) return output run_model("shrouds herself in white and walks penitentially disguised as brotherly love through factories and parliaments; offers help, but desires power;") run_model("He thanked all fellow bloggers and organizations that showed support.") run_model("Races are held between April and December at the Veliefendi Hippodrome near Bakerky, 15 km (9 miles) west of Istanbul.") ``` which should result in the following: ``` ['What is the name of the man who is a brotherly love?'] ['What did He thank all fellow bloggers and organizations that showed support?'] ['Where is the Veliefendi Hippodrome located?'] ```
allenai/t5-small-squad11	allenai	2023-01-24T16:27:41Z	16	1	transformers	[ "transformers", "pytorch", "jax", "t5", "text2text-generation", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en --- SQuAD 1.1 question-answering based on T5-small. Example use: ```python from transformers import T5Config, T5ForConditionalGeneration, T5Tokenizer model_name = "allenai/t5-small-next-word-generator-qoogle" tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) return output run_model("Who is the winner of 2009 olympics? \n Jack and Jill participated, but James won the games.")``` which should result in the following: ``` ['James'] ```
allenai/led-large-16384-arxiv	allenai	2023-01-24T16:27:02Z	2,522	31	transformers	[ "transformers", "pytorch", "tf", "led", "text2text-generation", "en", "dataset:scientific_papers", "arxiv:2004.05150", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: en datasets: - scientific_papers license: apache-2.0 --- ## Introduction [Allenai's Longformer Encoder-Decoder (LED)](https://github.com/allenai/longformer#longformer). This is the official led-large-16384 checkpoint that is fine-tuned on the arXiv dataset.led-large-16384-arxiv is the official fine-tuned version of [led-large-16384](https://huggingface.co/allenai/led-large-16384). As presented in the [paper](https://arxiv.org/pdf/2004.05150.pdf), the checkpoint achieves state-of-the-art results on arxiv ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/led_arxiv_result.png) ## Evaluation on downstream task [This notebook](https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing) shows how led-large-16384-arxiv can be evaluated on the [arxiv dataset](https://huggingface.co/datasets/scientific_papers) ## Usage The model can be used as follows. The input is taken from the test data of the [arxiv dataset](https://huggingface.co/datasets/scientific_papers). ```python LONG_ARTICLE = """"for about 20 years the problem of properties of short - term changes of solar activity has been considered extensively . many investigators studied the short - term periodicities of the various indices of solar activity . several periodicities were detected , but the periodicities about 155 days and from the interval of @xmath3 $ ] days ( @xmath4 $ ] years ) are mentioned most often . first of them was discovered by @xcite in the occurence rate of gamma - ray flares detected by the gamma - ray spectrometer aboard the _ solar maximum mission ( smm ) . this periodicity was confirmed for other solar flares data and for the same time period @xcite . it was also found in proton flares during solar cycles 19 and 20 @xcite , but it was not found in the solar flares data during solar cycles 22 @xcite . _ several autors confirmed above results for the daily sunspot area data . @xcite studied the sunspot data from 18741984 . she found the 155-day periodicity in data records from 31 years . this periodicity is always characteristic for one of the solar hemispheres ( the southern hemisphere for cycles 1215 and the northern hemisphere for cycles 1621 ) . moreover , it is only present during epochs of maximum activity ( in episodes of 13 years ) . similarinvestigationswerecarriedoutby + @xcite . they applied the same power spectrum method as lean , but the daily sunspot area data ( cycles 1221 ) were divided into 10 shorter time series . the periodicities were searched for the frequency interval 57115 nhz ( 100200 days ) and for each of 10 time series . the authors showed that the periodicity between 150160 days is statistically significant during all cycles from 16 to 21 . the considered peaks were remained unaltered after removing the 11-year cycle and applying the power spectrum analysis . @xcite used the wavelet technique for the daily sunspot areas between 1874 and 1993 . they determined the epochs of appearance of this periodicity and concluded that it presents around the maximum activity period in cycles 16 to 21 . moreover , the power of this periodicity started growing at cycle 19 , decreased in cycles 20 and 21 and disappered after cycle 21 . similaranalyseswerepresentedby + @xcite , but for sunspot number , solar wind plasma , interplanetary magnetic field and geomagnetic activity index @xmath5 . during 1964 - 2000 the sunspot number wavelet power of periods less than one year shows a cyclic evolution with the phase of the solar cycle.the 154-day period is prominent and its strenth is stronger around the 1982 - 1984 interval in almost all solar wind parameters . the existence of the 156-day periodicity in sunspot data were confirmed by @xcite . they considered the possible relation between the 475-day ( 1.3-year ) and 156-day periodicities . the 475-day ( 1.3-year ) periodicity was also detected in variations of the interplanetary magnetic field , geomagnetic activity helioseismic data and in the solar wind speed @xcite . @xcite concluded that the region of larger wavelet power shifts from 475-day ( 1.3-year ) period to 620-day ( 1.7-year ) period and then back to 475-day ( 1.3-year ) . the periodicities from the interval @xmath6 $ ] days ( @xmath4 $ ] years ) have been considered from 1968 . @xcite mentioned a 16.3-month ( 490-day ) periodicity in the sunspot numbers and in the geomagnetic data . @xcite analysed the occurrence rate of major flares during solar cycles 19 . they found a 18-month ( 540-day ) periodicity in flare rate of the norhern hemisphere . @xcite confirmed this result for the @xmath7 flare data for solar cycles 20 and 21 and found a peak in the power spectra near 510540 days . @xcite found a 17-month ( 510-day ) periodicity of sunspot groups and their areas from 1969 to 1986 . these authors concluded that the length of this period is variable and the reason of this periodicity is still not understood . @xcite and + @xcite obtained statistically significant peaks of power at around 158 days for daily sunspot data from 1923 - 1933 ( cycle 16 ) . in this paper the problem of the existence of this periodicity for sunspot data from cycle 16 is considered . the daily sunspot areas , the mean sunspot areas per carrington rotation , the monthly sunspot numbers and their fluctuations , which are obtained after removing the 11-year cycle are analysed . in section 2 the properties of the power spectrum methods are described . in section 3 a new approach to the problem of aliases in the power spectrum analysis is presented . in section 4 numerical results of the new method of the diagnosis of an echo - effect for sunspot area data are discussed . in section 5 the problem of the existence of the periodicity of about 155 days during the maximum activity period for sunspot data from the whole solar disk and from each solar hemisphere separately is considered . to find periodicities in a given time series the power spectrum analysis is applied . in this paper two methods are used : the fast fourier transformation algorithm with the hamming window function ( fft ) and the blackman - tukey ( bt ) power spectrum method @xcite . the bt method is used for the diagnosis of the reasons of the existence of peaks , which are obtained by the fft method . the bt method consists in the smoothing of a cosine transform of an autocorrelation function using a 3-point weighting average . such an estimator is consistent and unbiased . moreover , the peaks are uncorrelated and their sum is a variance of a considered time series . the main disadvantage of this method is a weak resolution of the periodogram points , particularly for low frequences . for example , if the autocorrelation function is evaluated for @xmath8 , then the distribution points in the time domain are : @xmath9 thus , it is obvious that this method should not be used for detecting low frequency periodicities with a fairly good resolution . however , because of an application of the autocorrelation function , the bt method can be used to verify a reality of peaks which are computed using a method giving the better resolution ( for example the fft method ) . it is valuable to remember that the power spectrum methods should be applied very carefully . the difficulties in the interpretation of significant peaks could be caused by at least four effects : a sampling of a continuos function , an echo - effect , a contribution of long - term periodicities and a random noise . first effect exists because periodicities , which are shorter than the sampling interval , may mix with longer periodicities . in result , this effect can be reduced by an decrease of the sampling interval between observations . the echo - effect occurs when there is a latent harmonic of frequency @xmath10 in the time series , giving a spectral peak at @xmath10 , and also periodic terms of frequency @xmath11 etc . this may be detected by the autocorrelation function for time series with a large variance . time series often contain long - term periodicities , that influence short - term peaks . they could rise periodogram s peaks at lower frequencies . however , it is also easy to notice the influence of the long - term periodicities on short - term peaks in the graphs of the autocorrelation functions . this effect is observed for the time series of solar activity indexes which are limited by the 11-year cycle . to find statistically significant periodicities it is reasonable to use the autocorrelation function and the power spectrum method with a high resolution . in the case of a stationary time series they give similar results . moreover , for a stationary time series with the mean zero the fourier transform is equivalent to the cosine transform of an autocorrelation function @xcite . thus , after a comparison of a periodogram with an appropriate autocorrelation function one can detect peaks which are in the graph of the first function and do not exist in the graph of the second function . the reasons of their existence could be explained by the long - term periodicities and the echo - effect . below method enables one to detect these effects . ( solid line ) and the 95% confidence level basing on thered noise ( dotted line ) . the periodogram values are presented on the left axis . the lower curve illustrates the autocorrelation function of the same time series ( solid line ) . the dotted lines represent two standard errors of the autocorrelation function . the dashed horizontal line shows the zero level . the autocorrelation values are shown in the right axis . ] because the statistical tests indicate that the time series is a white noise the confidence level is not marked . ] . ] the method of the diagnosis of an echo - effect in the power spectrum ( de ) consists in an analysis of a periodogram of a given time series computed using the bt method . the bt method bases on the cosine transform of the autocorrelation function which creates peaks which are in the periodogram , but not in the autocorrelation function . the de method is used for peaks which are computed by the fft method ( with high resolution ) and are statistically significant . the time series of sunspot activity indexes with the spacing interval one rotation or one month contain a markov - type persistence , which means a tendency for the successive values of the time series to remember their antecendent values . thus , i use a confidence level basing on the red noise of markov @xcite for the choice of the significant peaks of the periodogram computed by the fft method . when a time series does not contain the markov - type persistence i apply the fisher test and the kolmogorov - smirnov test at the significance level @xmath12 @xcite to verify a statistically significance of periodograms peaks . the fisher test checks the null hypothesis that the time series is white noise agains the alternative hypothesis that the time series contains an added deterministic periodic component of unspecified frequency . because the fisher test tends to be severe in rejecting peaks as insignificant the kolmogorov - smirnov test is also used . the de method analyses raw estimators of the power spectrum . they are given as follows @xmath13 for @xmath14 + where @xmath15 for @xmath16 + @xmath17 is the length of the time series @xmath18 and @xmath19 is the mean value . the first term of the estimator @xmath20 is constant . the second term takes two values ( depending on odd or even @xmath21 ) which are not significant because @xmath22 for large m. thus , the third term of ( 1 ) should be analysed . looking for intervals of @xmath23 for which @xmath24 has the same sign and different signs one can find such parts of the function @xmath25 which create the value @xmath20 . let the set of values of the independent variable of the autocorrelation function be called @xmath26 and it can be divided into the sums of disjoint sets : @xmath27 where + @xmath28 + @xmath29 @xmath30 @xmath31 + @xmath32 + @xmath33 @xmath34 @xmath35 @xmath36 @xmath37 @xmath38 @xmath39 @xmath40 well , the set @xmath41 contains all integer values of @xmath23 from the interval of @xmath42 for which the autocorrelation function and the cosinus function with the period @xmath43 $ ] are positive . the index @xmath44 indicates successive parts of the cosinus function for which the cosinuses of successive values of @xmath23 have the same sign . however , sometimes the set @xmath41 can be empty . for example , for @xmath45 and @xmath46 the set @xmath47 should contain all @xmath48 $ ] for which @xmath49 and @xmath50 , but for such values of @xmath23 the values of @xmath51 are negative . thus , the set @xmath47 is empty . . the periodogram values are presented on the left axis . the lower curve illustrates the autocorrelation function of the same time series . the autocorrelation values are shown in the right axis . ] let us take into consideration all sets \{@xmath52 } , \{@xmath53 } and \{@xmath41 } which are not empty . because numberings and power of these sets depend on the form of the autocorrelation function of the given time series , it is impossible to establish them arbitrary . thus , the sets of appropriate indexes of the sets \{@xmath52 } , \{@xmath53 } and \{@xmath41 } are called @xmath54 , @xmath55 and @xmath56 respectively . for example the set @xmath56 contains all @xmath44 from the set @xmath57 for which the sets @xmath41 are not empty . to separate quantitatively in the estimator @xmath20 the positive contributions which are originated by the cases described by the formula ( 5 ) from the cases which are described by the formula ( 3 ) the following indexes are introduced : @xmath58 @xmath59 @xmath60 @xmath61 where @xmath62 @xmath63 @xmath64 taking for the empty sets \{@xmath53 } and \{@xmath41 } the indices @xmath65 and @xmath66 equal zero . the index @xmath65 describes a percentage of the contribution of the case when @xmath25 and @xmath51 are positive to the positive part of the third term of the sum ( 1 ) . the index @xmath66 describes a similar contribution , but for the case when the both @xmath25 and @xmath51 are simultaneously negative . thanks to these one can decide which the positive or the negative values of the autocorrelation function have a larger contribution to the positive values of the estimator @xmath20 . when the difference @xmath67 is positive , the statement the @xmath21-th peak really exists can not be rejected . thus , the following formula should be satisfied : @xmath68 because the @xmath21-th peak could exist as a result of the echo - effect , it is necessary to verify the second condition : @xmath69\in c_m.\ ] ] . the periodogram values are presented on the left axis . the lower curve illustrates the autocorrelation function of the same time series ( solid line ) . the dotted lines represent two standard errors of the autocorrelation function . the dashed horizontal line shows the zero level . the autocorrelation values are shown in the right axis . ] to verify the implication ( 8) firstly it is necessary to evaluate the sets @xmath41 for @xmath70 of the values of @xmath23 for which the autocorrelation function and the cosine function with the period @xmath71 $ ] are positive and the sets @xmath72 of values of @xmath23 for which the autocorrelation function and the cosine function with the period @xmath43 $ ] are negative . secondly , a percentage of the contribution of the sum of products of positive values of @xmath25 and @xmath51 to the sum of positive products of the values of @xmath25 and @xmath51 should be evaluated . as a result the indexes @xmath65 for each set @xmath41 where @xmath44 is the index from the set @xmath56 are obtained . thirdly , from all sets @xmath41 such that @xmath70 the set @xmath73 for which the index @xmath65 is the greatest should be chosen . the implication ( 8) is true when the set @xmath73 includes the considered period @xmath43 $ ] . this means that the greatest contribution of positive values of the autocorrelation function and positive cosines with the period @xmath43 $ ] to the periodogram value @xmath20 is caused by the sum of positive products of @xmath74 for each @xmath75-\frac{m}{2k},[\frac{ 2m}{k}]+\frac{m}{2k})$ ] . when the implication ( 8) is false , the peak @xmath20 is mainly created by the sum of positive products of @xmath74 for each @xmath76-\frac{m}{2k},\big [ \frac{2m}{n}\big ] + \frac{m}{2k } \big ) $ ] , where @xmath77 is a multiple or a divisor of @xmath21 . it is necessary to add , that the de method should be applied to the periodograms peaks , which probably exist because of the echo - effect . it enables one to find such parts of the autocorrelation function , which have the significant contribution to the considered peak . the fact , that the conditions ( 7 ) and ( 8) are satisfied , can unambiguously decide about the existence of the considered periodicity in the given time series , but if at least one of them is not satisfied , one can doubt about the existence of the considered periodicity . thus , in such cases the sentence the peak can not be treated as true should be used . using the de method it is necessary to remember about the power of the set @xmath78 . if @xmath79 is too large , errors of an autocorrelation function estimation appear . they are caused by the finite length of the given time series and as a result additional peaks of the periodogram occur . if @xmath79 is too small , there are less peaks because of a low resolution of the periodogram . in applications @xmath80 is used . in order to evaluate the value @xmath79 the fft method is used . the periodograms computed by the bt and the fft method are compared . the conformity of them enables one to obtain the value @xmath79 . . the fft periodogram values are presented on the left axis . the lower curve illustrates the bt periodogram of the same time series ( solid line and large black circles ) . the bt periodogram values are shown in the right axis . ] in this paper the sunspot activity data ( august 1923 - october 1933 ) provided by the greenwich photoheliographic results ( gpr ) are analysed . firstly , i consider the monthly sunspot number data . to eliminate the 11-year trend from these data , the consecutively smoothed monthly sunspot number @xmath81 is subtracted from the monthly sunspot number @xmath82 where the consecutive mean @xmath83 is given by @xmath84 the values @xmath83 for @xmath85 and @xmath86 are calculated using additional data from last six months of cycle 15 and first six months of cycle 17 . because of the north - south asymmetry of various solar indices @xcite , the sunspot activity is considered for each solar hemisphere separately . analogously to the monthly sunspot numbers , the time series of sunspot areas in the northern and southern hemispheres with the spacing interval @xmath87 rotation are denoted . in order to find periodicities , the following time series are used : + @xmath88 + @xmath89 + @xmath90 + in the lower part of figure [ f1 ] the autocorrelation function of the time series for the northern hemisphere @xmath88 is shown . it is easy to notice that the prominent peak falls at 17 rotations interval ( 459 days ) and @xmath25 for @xmath91 $ ] rotations ( [ 81 , 162 ] days ) are significantly negative . the periodogram of the time series @xmath88 ( see the upper curve in figures [ f1 ] ) does not show the significant peaks at @xmath92 rotations ( 135 , 162 days ) , but there is the significant peak at @xmath93 ( 243 days ) . the peaks at @xmath94 are close to the peaks of the autocorrelation function . thus , the result obtained for the periodicity at about @xmath0 days are contradict to the results obtained for the time series of daily sunspot areas @xcite . for the southern hemisphere ( the lower curve in figure [ f2 ] ) @xmath25 for @xmath95 $ ] rotations ( [ 54 , 189 ] days ) is not positive except @xmath96 ( 135 days ) for which @xmath97 is not statistically significant . the upper curve in figures [ f2 ] presents the periodogram of the time series @xmath89 . this time series does not contain a markov - type persistence . moreover , the kolmogorov - smirnov test and the fisher test do not reject a null hypothesis that the time series is a white noise only . this means that the time series do not contain an added deterministic periodic component of unspecified frequency . the autocorrelation function of the time series @xmath90 ( the lower curve in figure [ f3 ] ) has only one statistically significant peak for @xmath98 months ( 480 days ) and negative values for @xmath99 $ ] months ( [ 90 , 390 ] days ) . however , the periodogram of this time series ( the upper curve in figure [ f3 ] ) has two significant peaks the first at 15.2 and the second at 5.3 months ( 456 , 159 days ) . thus , the periodogram contains the significant peak , although the autocorrelation function has the negative value at @xmath100 months . to explain these problems two following time series of daily sunspot areas are considered : + @xmath101 + @xmath102 + where @xmath103 the values @xmath104 for @xmath105 and @xmath106 are calculated using additional daily data from the solar cycles 15 and 17 . and the cosine function for @xmath45 ( the period at about 154 days ) . the horizontal line ( dotted line ) shows the zero level . the vertical dotted lines evaluate the intervals where the sets @xmath107 ( for @xmath108 ) are searched . the percentage values show the index @xmath65 for each @xmath41 for the time series @xmath102 ( in parentheses for the time series @xmath101 ) . in the right bottom corner the values of @xmath65 for the time series @xmath102 , for @xmath109 are written . ] ( the 500-day period ) ] the comparison of the functions @xmath25 of the time series @xmath101 ( the lower curve in figure [ f4 ] ) and @xmath102 ( the lower curve in figure [ f5 ] ) suggests that the positive values of the function @xmath110 of the time series @xmath101 in the interval of @xmath111 $ ] days could be caused by the 11-year cycle . this effect is not visible in the case of periodograms of the both time series computed using the fft method ( see the upper curves in figures [ f4 ] and [ f5 ] ) or the bt method ( see the lower curve in figure [ f6 ] ) . moreover , the periodogram of the time series @xmath102 has the significant values at @xmath112 days , but the autocorrelation function is negative at these points . @xcite showed that the lomb - scargle periodograms for the both time series ( see @xcite , figures 7 a - c ) have a peak at 158.8 days which stands over the fap level by a significant amount . using the de method the above discrepancies are obvious . to establish the @xmath79 value the periodograms computed by the fft and the bt methods are shown in figure [ f6 ] ( the upper and the lower curve respectively ) . for @xmath46 and for periods less than 166 days there is a good comformity of the both periodograms ( but for periods greater than 166 days the points of the bt periodogram are not linked because the bt periodogram has much worse resolution than the fft periodogram ( no one know how to do it ) ) . for @xmath46 and @xmath113 the value of @xmath21 is 13 ( @xmath71=153 $ ] ) . the inequality ( 7 ) is satisfied because @xmath114 . this means that the value of @xmath115 is mainly created by positive values of the autocorrelation function . the implication ( 8) needs an evaluation of the greatest value of the index @xmath65 where @xmath70 , but the solar data contain the most prominent period for @xmath116 days because of the solar rotation . thus , although @xmath117 for each @xmath118 , all sets @xmath41 ( see ( 5 ) and ( 6 ) ) without the set @xmath119 ( see ( 4 ) ) , which contains @xmath120 $ ] , are considered . this situation is presented in figure [ f7 ] . in this figure two curves @xmath121 and @xmath122 are plotted . the vertical dotted lines evaluate the intervals where the sets @xmath107 ( for @xmath123 ) are searched . for such @xmath41 two numbers are written : in parentheses the value of @xmath65 for the time series @xmath101 and above it the value of @xmath65 for the time series @xmath102 . to make this figure clear the curves are plotted for the set @xmath124 only . ( in the right bottom corner information about the values of @xmath65 for the time series @xmath102 , for @xmath109 are written . ) the implication ( 8) is not true , because @xmath125 for @xmath126 . therefore , @xmath43=153\notin c_6=[423,500]$ ] . moreover , the autocorrelation function for @xmath127 $ ] is negative and the set @xmath128 is empty . thus , @xmath129 . on the basis of these information one can state , that the periodogram peak at @xmath130 days of the time series @xmath102 exists because of positive @xmath25 , but for @xmath23 from the intervals which do not contain this period . looking at the values of @xmath65 of the time series @xmath101 , one can notice that they decrease when @xmath23 increases until @xmath131 . this indicates , that when @xmath23 increases , the contribution of the 11-year cycle to the peaks of the periodogram decreases . an increase of the value of @xmath65 is for @xmath132 for the both time series , although the contribution of the 11-year cycle for the time series @xmath101 is insignificant . thus , this part of the autocorrelation function ( @xmath133 for the time series @xmath102 ) influences the @xmath21-th peak of the periodogram . this suggests that the periodicity at about 155 days is a harmonic of the periodicity from the interval of @xmath1 $ ] days . ( solid line ) and consecutively smoothed sunspot areas of the one rotation time interval @xmath134 ( dotted line ) . both indexes are presented on the left axis . the lower curve illustrates fluctuations of the sunspot areas @xmath135 . the dotted and dashed horizontal lines represent levels zero and @xmath136 respectively . the fluctuations are shown on the right axis . ] the described reasoning can be carried out for other values of the periodogram . for example , the condition ( 8) is not satisfied for @xmath137 ( 250 , 222 , 200 days ) . moreover , the autocorrelation function at these points is negative . these suggest that there are not a true periodicity in the interval of [ 200 , 250 ] days . it is difficult to decide about the existence of the periodicities for @xmath138 ( 333 days ) and @xmath139 ( 286 days ) on the basis of above analysis . the implication ( 8) is not satisfied for @xmath139 and the condition ( 7 ) is not satisfied for @xmath138 , although the function @xmath25 of the time series @xmath102 is significantly positive for @xmath140 . the conditions ( 7 ) and ( 8) are satisfied for @xmath141 ( figure [ f8 ] ) and @xmath142 . therefore , it is possible to exist the periodicity from the interval of @xmath1 $ ] days . similar results were also obtained by @xcite for daily sunspot numbers and daily sunspot areas . she considered the means of three periodograms of these indexes for data from @xmath143 years and found statistically significant peaks from the interval of @xmath1 $ ] ( see @xcite , figure 2 ) . @xcite studied sunspot areas from 1876 - 1999 and sunspot numbers from 1749 - 2001 with the help of the wavelet transform . they pointed out that the 154 - 158-day period could be the third harmonic of the 1.3-year ( 475-day ) period . moreover , the both periods fluctuate considerably with time , being stronger during stronger sunspot cycles . therefore , the wavelet analysis suggests a common origin of the both periodicities . this conclusion confirms the de method result which indicates that the periodogram peak at @xmath144 days is an alias of the periodicity from the interval of @xmath1 $ ] in order to verify the existence of the periodicity at about 155 days i consider the following time series : + @xmath145 + @xmath146 + @xmath147 + the value @xmath134 is calculated analogously to @xmath83 ( see sect . the values @xmath148 and @xmath149 are evaluated from the formula ( 9 ) . in the upper part of figure [ f9 ] the time series of sunspot areas @xmath150 of the one rotation time interval from the whole solar disk and the time series of consecutively smoothed sunspot areas @xmath151 are showed . in the lower part of figure [ f9 ] the time series of sunspot area fluctuations @xmath145 is presented . on the basis of these data the maximum activity period of cycle 16 is evaluated . it is an interval between two strongest fluctuations e.a . @xmath152 $ ] rotations . the length of the time interval @xmath153 is 54 rotations . if the about @xmath0-day ( 6 solar rotations ) periodicity existed in this time interval and it was characteristic for strong fluctuations from this time interval , 10 local maxima in the set of @xmath154 would be seen . then it should be necessary to find such a value of p for which @xmath155 for @xmath156 and the number of the local maxima of these values is 10 . as it can be seen in the lower part of figure [ f9 ] this is for the case of @xmath157 ( in this figure the dashed horizontal line is the level of @xmath158 ) . figure [ f10 ] presents nine time distances among the successive fluctuation local maxima and the horizontal line represents the 6-rotation periodicity . it is immediately apparent that the dispersion of these points is 10 and it is difficult to find even few points which oscillate around the value of 6 . such an analysis was carried out for smaller and larger @xmath136 and the results were similar . therefore , the fact , that the about @xmath0-day periodicity exists in the time series of sunspot area fluctuations during the maximum activity period is questionable . . the horizontal line represents the 6-rotation ( 162-day ) period . ] ] ] to verify again the existence of the about @xmath0-day periodicity during the maximum activity period in each solar hemisphere separately , the time series @xmath88 and @xmath89 were also cut down to the maximum activity period ( january 1925december 1930 ) . the comparison of the autocorrelation functions of these time series with the appriopriate autocorrelation functions of the time series @xmath88 and @xmath89 , which are computed for the whole 11-year cycle ( the lower curves of figures [ f1 ] and [ f2 ] ) , indicates that there are not significant differences between them especially for @xmath23=5 and 6 rotations ( 135 and 162 days ) ) . this conclusion is confirmed by the analysis of the time series @xmath146 for the maximum activity period . the autocorrelation function ( the lower curve of figure [ f11 ] ) is negative for the interval of [ 57 , 173 ] days , but the resolution of the periodogram is too low to find the significant peak at @xmath159 days . the autocorrelation function gives the same result as for daily sunspot area fluctuations from the whole solar disk ( @xmath160 ) ( see also the lower curve of figures [ f5 ] ) . in the case of the time series @xmath89 @xmath161 is zero for the fluctuations from the whole solar cycle and it is almost zero ( @xmath162 ) for the fluctuations from the maximum activity period . the value @xmath163 is negative . similarly to the case of the northern hemisphere the autocorrelation function and the periodogram of southern hemisphere daily sunspot area fluctuations from the maximum activity period @xmath147 are computed ( see figure [ f12 ] ) . the autocorrelation function has the statistically significant positive peak in the interval of [ 155 , 165 ] days , but the periodogram has too low resolution to decide about the possible periodicities . the correlative analysis indicates that there are positive fluctuations with time distances about @xmath0 days in the maximum activity period . the results of the analyses of the time series of sunspot area fluctuations from the maximum activity period are contradict with the conclusions of @xcite . she uses the power spectrum analysis only . the periodogram of daily sunspot fluctuations contains peaks , which could be harmonics or subharmonics of the true periodicities . they could be treated as real periodicities . this effect is not visible for sunspot data of the one rotation time interval , but averaging could lose true periodicities . this is observed for data from the southern hemisphere . there is the about @xmath0-day peak in the autocorrelation function of daily fluctuations , but the correlation for data of the one rotation interval is almost zero or negative at the points @xmath164 and 6 rotations . thus , it is reasonable to research both time series together using the correlative and the power spectrum analyses . the following results are obtained : 1 . a new method of the detection of statistically significant peaks of the periodograms enables one to identify aliases in the periodogram . 2 . two effects cause the existence of the peak of the periodogram of the time series of sunspot area fluctuations at about @xmath0 days : the first is caused by the 27-day periodicity , which probably creates the 162-day periodicity ( it is a subharmonic frequency of the 27-day periodicity ) and the second is caused by statistically significant positive values of the autocorrelation function from the intervals of @xmath165 $ ] and @xmath166 $ ] days . the existence of the periodicity of about @xmath0 days of the time series of sunspot area fluctuations and sunspot area fluctuations from the northern hemisphere during the maximum activity period is questionable . the autocorrelation analysis of the time series of sunspot area fluctuations from the southern hemisphere indicates that the periodicity of about 155 days exists during the maximum activity period . i appreciate valuable comments from professor j. jakimiec .""" from transformers import LEDForConditionalGeneration, LEDTokenizer import torch tokenizer = LEDTokenizer.from_pretrained("allenai/led-large-16384-arxiv") input_ids = tokenizer(LONG_ARTICLE, return_tensors="pt").input_ids.to("cuda") global_attention_mask = torch.zeros_like(input_ids) # set global_attention_mask on first token global_attention_mask[:, 0] = 1 model = LEDForConditionalGeneration.from_pretrained("allenai/led-large-16384-arxiv", return_dict_in_generate=True).to("cuda") sequences = model.generate(input_ids, global_attention_mask=global_attention_mask).sequences summary = tokenizer.batch_decode(sequences) ```
ViktorDo/DistilBERT-POWO_Scratch	ViktorDo	2023-01-24T16:26:31Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-01-24T15:00:39Z	--- tags: - generated_from_trainer model-index: - name: DistilBERT-POWO_Scratch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DistilBERT-POWO_Scratch This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9068 ## 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: 5 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 7.104 \| 0.18 \| 200 \| 5.9641 \| \| 5.6973 \| 0.36 \| 400 \| 5.5992 \| \| 5.5464 \| 0.54 \| 600 \| 5.4564 \| \| 5.377 \| 0.72 \| 800 \| 5.3606 \| \| 5.2162 \| 0.9 \| 1000 \| 5.2674 \| \| 5.1499 \| 1.08 \| 1200 \| 5.2080 \| \| 5.1313 \| 1.26 \| 1400 \| 5.1447 \| \| 5.0138 \| 1.44 \| 1600 \| 5.1041 \| \| 4.9509 \| 1.62 \| 1800 \| 5.0572 \| \| 4.9598 \| 1.8 \| 2000 \| 5.0185 \| \| 4.9581 \| 1.98 \| 2200 \| 5.0109 \| \| 4.8458 \| 2.16 \| 2400 \| 4.9608 \| \| 4.953 \| 2.34 \| 2600 \| 4.9482 \| \| 4.7448 \| 2.52 \| 2800 \| 4.9211 \| \| 4.8574 \| 2.71 \| 3000 \| 4.9093 \| \| 4.8402 \| 2.89 \| 3200 \| 4.8980 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
SZTAKI-HLT/mT5-small-HunSum-1	SZTAKI-HLT	2023-01-24T16:21:41Z	11	1	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "hu", "dataset:SZTAKI-HLT/HunSum-1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-01-06T10:17:13Z	--- language: - hu pipeline_tag: summarization inference: parameters: num_beams: 5 length_penalty: 2 max_length: 128 encoder_no_repeat_ngram_size: 4 no_repeat_ngram_size: 3 datasets: - SZTAKI-HLT/HunSum-1 metrics: - rouge --- # Model Card for mT5-small-HunSum-1 The mT5-small-HunSum-1 is a Hungarian abstractive summarization model, which was trained on the [SZTAKI-HLT/HunSum-1 dataset](https://huggingface.co/datasets/SZTAKI-HLT/HunSum-1). The model is based on [google/mt5-small]([google/mt5-small](https://huggingface.co/google/mt5-small)). ## Intended uses & limitations - Model type: Text Summarization - Language(s) (NLP): Hungarian - Resource(s) for more information: - [GitHub Repo](https://github.com/dorinapetra/summarization) ## Parameters - Batch Size: 16 - Learning Rate: 5e-5 - Weight Decay: 0.01 - Warmup Steps: 3000 - Epochs: 10 - no_repeat_ngram_size: 3 - num_beams: 5 - early_stopping: False - encoder_no_repeat_ngram_size: 4 ## Results \| Metric \| Value \| \| :------------ \| :------------------------------------------ \| \| ROUGE-1 \| 36.49 \| \| ROUGE-2 \| 9.50 \| \| ROUGE-L \| 23.48 \| ## Citation If you use our model, please cite the following paper: ``` @inproceedings {HunSum-1, title = {{HunSum-1: an Abstractive Summarization Dataset for Hungarian}}, booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)}, year = {2023}, publisher = {Szegedi Tudományegyetem, Informatikai Intézet}, address = {Szeged, Magyarország}, author = {Barta, Botond and Lakatos, Dorina and Nagy, Attila and Nyist, Mil{\'{a}}n Konor and {\'{A}}cs, Judit}, pages = {231--243} } ```
SZTAKI-HLT/Bert2Bert-HunSum-1	SZTAKI-HLT	2023-01-24T16:21:16Z	5	2	transformers	[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "hubert", "bert", "summarization", "hu", "dataset:SZTAKI-HLT/HunSum-1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-07T10:35:58Z	--- datasets: - SZTAKI-HLT/HunSum-1 language: - hu metrics: - rouge pipeline_tag: text2text-generation inference: parameters: num_beams: 5 length_penalty: 2 max_length: 128 no_repeat_ngram_size: 3 early_stopping: True tags: - hubert - bert - summarization --- # Model Card for Bert2Bert-HunSum-1 The Bert2Bert-HunSum-1 is a Hungarian abstractive summarization model, which was trained on the [SZTAKI-HLT/HunSum-1 dataset](https://huggingface.co/datasets/SZTAKI-HLT/HunSum-1). The model is based on [SZTAKI-HLT/hubert-base-cc](https://huggingface.co/SZTAKI-HLT/hubert-base-cc). ## Intended uses & limitations - Model type: Text Summarization - Language(s) (NLP): Hungarian - Resource(s) for more information: - [GitHub Repo](https://github.com/dorinapetra/summarization) ## Parameters - Batch Size: 13 - Learning Rate: 5e-5 - Weight Decay: 0.01 - Warmup Steps: 16000 - Epochs: 15 - no_repeat_ngram_size: 3 - num_beams: 5 - early_stopping: True ## Results \| Metric \| Value \| \| :------------ \| :------------------------------------------ \| \| ROUGE-1 \| 28.52 \| \| ROUGE-2 \| 10.35 \| \| ROUGE-L \| 20.07 \| ## Citation If you use our model, please cite the following paper: ``` @inproceedings {HunSum-1, title = {{HunSum-1: an Abstractive Summarization Dataset for Hungarian}}, booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)}, year = {2023}, publisher = {Szegedi Tudományegyetem, Informatikai Intézet}, address = {Szeged, Magyarország}, author = {Barta, Botond and Lakatos, Dorina and Nagy, Attila and Nyist, Mil{\'{a}}n Konor and {\'{A}}cs, Judit}, pages = {231--243} } ```
cmdshiftenter/distilbert-base-uncased-finetuned-emotion	cmdshiftenter	2023-01-24T16:06:29Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-24T14:35:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: train args: split metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9240228561413785 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-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: 0.2164 - Accuracy: 0.924 - F1: 0.9240 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8345 \| 1.0 \| 250 \| 0.2964 \| 0.909 \| 0.9074 \| \| 0.2422 \| 2.0 \| 500 \| 0.2164 \| 0.924 \| 0.9240 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
BobMcDear/regnety_3200mf	BobMcDear	2023-01-24T16:05:40Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:59Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnety_4000mf	BobMcDear	2023-01-24T16:05:33Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:42:00Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnetx_1600mf	BobMcDear	2023-01-24T16:05:08Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:42:01Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnety_6400mf	BobMcDear	2023-01-24T16:05:00Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:42:02Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnety_16gf	BobMcDear	2023-01-24T16:04:52Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:51Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnetx_16gf	BobMcDear	2023-01-24T16:04:45Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:52Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnetx_12gf	BobMcDear	2023-01-24T16:04:31Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:42:04Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnety_32gf	BobMcDear	2023-01-24T16:04:24Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:49Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnety_200mf	BobMcDear	2023-01-24T16:03:45Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:42:06Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
LarryAIDraw/7thtwamMixedmodel_7thtwam10	LarryAIDraw	2023-01-24T16:03:25Z	0	6	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-01-24T09:36:28Z	--- license: creativeml-openrail-m --- https://civitai.com/models/4870/7thtwam-mixedmodel
BobMcDear/regnety_12gf	BobMcDear	2023-01-24T16:03:23Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:45Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnetx_800mf	BobMcDear	2023-01-24T16:03:08Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:46Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
BobMcDear/regnety_600mf	BobMcDear	2023-01-24T16:02:59Z	0	0	null	[ "region:us" ]	null	2023-01-24T14:41:50Z	Please refer to [flaim](https://github.com/bobmcdear/flaim) for sample usage and more information.
arputtick/GPT_Neo_muslim_travel	arputtick	2023-01-24T15:55:27Z	40	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "en", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T16:37:52Z	--- language: en license: openrail pipeline_tag: text-generation --- # GPT-Neo 1.3B - Muslim Traveler ## Model Description GPT-Neo 1.3B-Muslim Traveler is finetuned on EleutherAI's GPT-Neo 1.3B model. ## Training data The training data consists of travel texts written by ancient muslim travelers. See 'combined.txt' file in the model repository. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='arputtick/GPT_Neo_muslim_travel') >>> generator("> You wake up.", do_sample=True, min_length=50) [{'generated_text': '> You wake up"\nYou get out of bed, don your armor and get out of the door in search for new adventures.'}] ``` ### Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ### BibTeX entry and citation info The model is made using the following software: ```bibtex @software{gpt-neo, author = {Black, Sid and Leo, Gao and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}}, month = mar, year = 2021, note = {{If you use this software, please cite it using these metadata.}}, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.5297715}, url = {https://doi.org/10.5281/zenodo.5297715} } ```
SorinAbrudan/q-FrozenLake-v1-4x4-noSlippery	SorinAbrudan	2023-01-24T15:41:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T15:41:04Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="SorinAbrudan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
khaled5321/worm	khaled5321	2023-01-24T15:15:07Z	12	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Worm", "region:us" ]	reinforcement-learning	2023-01-24T15:15:01Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Worm library_name: ml-agents --- # ppo Agent playing Worm This is a trained model of a ppo agent playing Worm using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Worm 2. Step 1: Write your model_id: khaled5321/worm 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
alphahg/kobigbird-pure2-93753787	alphahg	2023-01-24T15:14:56Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "big_bird", "question-answering", "generated_from_trainer", "dataset:custom_squad_v2", "endpoints_compatible", "region:us" ]	question-answering	2023-01-24T13:55:52Z	--- tags: - generated_from_trainer datasets: - custom_squad_v2 model-index: - name: kobigbird-pure2-93753787 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kobigbird-pure2-93753787 This model is a fine-tuned version of [monologg/kobigbird-bert-base](https://huggingface.co/monologg/kobigbird-bert-base) on the custom_squad_v2 dataset. It achieves the following results on the evaluation set: - eval_loss: 3.4619 - eval_runtime: 26.5163 - eval_samples_per_second: 45.406 - eval_steps_per_second: 0.717 - epoch: 0.99 - step: 21 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
Schwarzschild009/q-Taxi-v3	Schwarzschild009	2023-01-24T15:11:10Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T14:06:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Schwarzschild009/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
javiervela/dqn-SpaceInvadersNoFrameskip-v4	javiervela	2023-01-24T15:05:18Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-24T09:44:16Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 544.50 +/- 73.26 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga javiervela -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga javiervela -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga javiervela ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
Kenemo/ppo-LunarLander-v2-10Msteps	Kenemo	2023-01-24T15:00:29Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T14:56:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 278.51 +/- 17.32 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
AllaNBarakat/xlm-roberta-base-fintuned-panx-de	AllaNBarakat	2023-01-24T14:52:43Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-24T14:35:48Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-fintuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: train args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8647266113447767 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-fintuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1357 - F1: 0.8647 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2577 \| 1.0 \| 525 \| 0.1719 \| 0.8077 \| \| 0.1254 \| 2.0 \| 1050 \| 0.1362 \| 0.8558 \| \| 0.081 \| 3.0 \| 1575 \| 0.1357 \| 0.8647 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
FacebookAI/xlm-mlm-enro-1024	FacebookAI	2023-01-24T14:50:13Z	111	0	transformers	[ "transformers", "pytorch", "tf", "xlm", "fill-mask", "multilingual", "en", "ro", "arxiv:1901.07291", "arxiv:1910.09700", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: - multilingual - en - ro license: cc-by-nc-4.0 --- # xlm-mlm-enro-1024 # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Technical Specifications](#technical-specifications) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) 10. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample, Alexis Conneau. xlm-mlm-enro-1024 is a transformer pretrained using a masked language modeling (MLM) objective for English-Romanian. This model uses language embeddings to specify the language used at inference. See the [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) for further details. ## Model Description - Developed by: Guillaume Lample, Alexis Conneau, see [associated paper](https://arxiv.org/abs/1901.07291) - Model type: Language model - Language(s) (NLP): English-Romanian - License: license: cc-by-nc-4.0 - Related Models: [xlm-clm-enfr-1024](https://huggingface.co/xlm-clm-enfr-1024), [xlm-clm-ende-1024](https://huggingface.co/xlm-clm-ende-1024), [xlm-mlm-enfr-1024](https://huggingface.co/xlm-mlm-enfr-1024), [xlm-mlm-ende-1024](https://huggingface.co/xlm-mlm-ende-1024) - Resources for more information: - [Associated paper](https://arxiv.org/abs/1901.07291) - [GitHub Repo](https://github.com/facebookresearch/XLM) - [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) # Uses ## Direct Use The model is a language model. The model can be used for masked language modeling. ## Downstream Use To learn more about this task and potential downstream uses, see the Hugging Face [fill mask docs](https://huggingface.co/tasks/fill-mask) and the [Hugging Face Multilingual Models for Inference](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) docs. ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations 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)). ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training The model developers write: > In all experiments, we use a Transformer architecture with 1024 hidden units, 8 heads, GELU activations (Hendrycks and Gimpel, 2016), a dropout rate of 0.1 and learned positional embeddings. We train our models with the Adam op- timizer (Kingma and Ba, 2014), a linear warm- up (Vaswani et al., 2017) and learning rates varying from 10^−4 to 5.10^−4. See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for links, citations, and further details on the training data and training procedure. The model developers also write that: > If you use these models, you should use the same data preprocessing / BPE codes to preprocess your data. See the associated [GitHub Repo](https://github.com/facebookresearch/XLM#ii-cross-lingual-language-model-pretraining-xlm) for further details. # Evaluation ## Testing Data, Factors & Metrics The model developers evaluated the model on the [WMT'16 English-Romanian](https://huggingface.co/datasets/wmt16) dataset using the [BLEU metric](https://huggingface.co/spaces/evaluate-metric/bleu). See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for further details on the testing data, factors and metrics. ## Results For xlm-mlm-enro-1024 results, see Tables 1-3 of the [associated paper](https://arxiv.org/pdf/1901.07291.pdf). # 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). - Hardware Type: More information needed - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications The model developers write: > We implement all our models in PyTorch (Paszke et al., 2017), and train them on 64 Volta GPUs for the language modeling tasks, and 8 GPUs for the MT tasks. We use float16 operations to speed up training and to reduce the memory usage of our models. See the [associated paper](https://arxiv.org/pdf/1901.07291.pdf) for further details. # Citation BibTeX: ```bibtex @article{lample2019cross, title={Cross-lingual language model pretraining}, author={Lample, Guillaume and Conneau, Alexis}, journal={arXiv preprint arXiv:1901.07291}, year={2019} } ``` APA: - Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model More information needed. This model uses language embeddings to specify the language used at inference. See the [Hugging Face Multilingual Models for Inference docs](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) for further details.
FacebookAI/xlm-mlm-en-2048	FacebookAI	2023-01-24T14:50:04Z	1,953	0	transformers	[ "transformers", "pytorch", "tf", "xlm", "fill-mask", "exbert", "en", "arxiv:1901.07291", "arxiv:1911.02116", "arxiv:1910.09700", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: en tags: - exbert license: cc-by-nc-4.0 --- # xlm-mlm-en-2048 # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Citation](#citation) 8. [Model Card Authors](#model-card-authors) 9. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details The XLM model was proposed in [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau. It’s a transformer pretrained with either a causal language modeling (CLM) objective (next token prediction), a masked language modeling (MLM) objective (BERT-like), or a Translation Language Modeling (TLM) object (extension of BERT’s MLM to multiple language inputs). This model is trained with a masked language modeling objective on English text. ## Model Description - Developed by: Researchers affiliated with Facebook AI, see [associated paper](https://arxiv.org/abs/1901.07291) and [GitHub Repo](https://github.com/facebookresearch/XLM) - Model type: Language model - Language(s) (NLP): English - License: CC-BY-NC-4.0 - Related Models: Other [XLM models](https://huggingface.co/models?sort=downloads&search=xlm) - Resources for more information: - [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau (2019) - [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/pdf/1911.02116.pdf) by Conneau et al. (2020) - [GitHub Repo](https://github.com/facebookresearch/XLM) - [Hugging Face XLM docs](https://huggingface.co/docs/transformers/model_doc/xlm) # Uses ## Direct Use The model is a language model. The model can be used for masked language modeling. ## Downstream Use To learn more about this task and potential downstream uses, see the Hugging Face [fill mask docs](https://huggingface.co/tasks/fill-mask) and the [Hugging Face Multilingual Models for Inference](https://huggingface.co/docs/transformers/v4.20.1/en/multilingual#xlm-with-language-embeddings) docs. Also see the [associated paper](https://arxiv.org/abs/1901.07291). ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations 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)). ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training More information needed. See the [associated GitHub Repo](https://github.com/facebookresearch/XLM). # Evaluation More information needed. See the [associated GitHub Repo](https://github.com/facebookresearch/XLM). # 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). - Hardware Type: More information needed - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Citation BibTeX: ```bibtex @article{lample2019cross, title={Cross-lingual language model pretraining}, author={Lample, Guillaume and Conneau, Alexis}, journal={arXiv preprint arXiv:1901.07291}, year={2019} } ``` APA: - Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. See the [Hugging Face XLM docs](https://huggingface.co/docs/transformers/model_doc/xlm) for more examples. ```python from transformers import XLMTokenizer, XLMModel import torch tokenizer = XLMTokenizer.from_pretrained("xlm-mlm-en-2048") model = XLMModel.from_pretrained("xlm-mlm-en-2048") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` <a href="https://huggingface.co/exbert/?model=xlm-mlm-en-2048"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
dobis-lks/sloth-animal	dobis-lks	2023-01-24T14:47:03Z	1	1	diffusers	[ "diffusers", "pytorch", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon", "animal", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-24T14:41:44Z	--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth-hackathon - animal widget: - text: a photo of sloth animal in the Acropolis --- # DreamBooth model for the sloth concept trained by dobis-lks on the dobis-lks/test dataset. This is a Stable Diffusion model fine-tuned on the sloth concept with DreamBooth. It can be used by modifying the `instance_prompt`: a photo of sloth animal This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `animal` images for the animal theme. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('dobis-lks/sloth-animal') image = pipeline().images[0] image ```
summervent/russian-spellchecking2	summervent	2023-01-24T14:45:44Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-24T14:36:37Z	--- tags: - generated_from_trainer model-index: - name: russian-spellchecking2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # russian-spellchecking2 This model is a fine-tuned version of [UrukHan/t5-russian-spell](https://huggingface.co/UrukHan/t5-russian-spell) on an unknown dataset. ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2
keras-sd/decoder-tflite	keras-sd	2023-01-24T14:41:02Z	0	0	keras	[ "keras", "tflite", "decoder", "stable diffusion", "v1.4", "text-to-image", "license:apache-2.0", "region:us" ]	text-to-image	2022-12-27T00:33:48Z	--- license: apache-2.0 library_name: keras pipeline_tag: text-to-image tags: - decoder - stable diffusion - v1.4 --- This repository hosts the TFLite version of `decoder` part of [KerasCV Stable Diffusion](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion). Stable Diffusion consists of `text encoder`, `diffusion model`, `decoder`, and some glue codes to handl inputs and outputs of each part. The TFLite version of `decoder` in this repository is built not only with the `decpder` itself but also TensorFlow operations that takes `latent` from `diffusion model` and generates final images. TFLite conversion was based on the `SavedModel` from [this repository](https://huggingface.co/keras-sd/tfs-text-encoder/tree/main), and TensorFlow version `>= 2.12-nightly` was used. - NOTE: [Dynamic range quantization](https://www.tensorflow.org/lite/performance/post_training_quant#optimizing_an_existing_model) was used. - NOTE: TensorFlow version `< 2.12-nightly` will fail for the conversion process. - NOTE: For those who wonder how `SavedModel` is constructed, find it in [keras-sd-serving repository](https://github.com/deep-diver/keras-sd-serving).
khaled5321/RND-PyramidsTraining	khaled5321	2023-01-24T14:40:39Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-24T14:39:30Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: khaled5321/RND-PyramidsTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay_from_local	Ashraf-kasem	2023-01-24T14:40:18Z	17	0	transformers	[ "transformers", "tf", "tensorboard", "gpt2", "text-generation", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-01-17T12:04:41Z	--- tags: - generated_from_keras_callback model-index: - name: Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay_from_local results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay_from_local This model is a fine-tuned version of [Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay_from_local](https://huggingface.co/Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay_from_local) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.4591 - Validation Loss: 4.1433 - Epoch: 49 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 231100, '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-07, 'amsgrad': False} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.0567 \| 3.4196 \| 0 \| \| 2.0328 \| 3.4604 \| 1 \| \| 2.0056 \| 3.5015 \| 2 \| \| 1.9789 \| 3.5125 \| 3 \| \| 1.9530 \| 3.5556 \| 4 \| \| 1.9285 \| 3.5970 \| 5 \| \| 1.9051 \| 3.6428 \| 6 \| \| 1.8823 \| 3.6087 \| 7 \| \| 1.8607 \| 3.6300 \| 8 \| \| 1.8402 \| 3.6607 \| 9 \| \| 1.8202 \| 3.7323 \| 10 \| \| 1.8014 \| 3.7363 \| 11 \| \| 1.7832 \| 3.7573 \| 12 \| \| 1.7660 \| 3.7414 \| 13 \| \| 1.7493 \| 3.7810 \| 14 \| \| 1.7330 \| 3.8443 \| 15 \| \| 1.7175 \| 3.8305 \| 16 \| \| 1.7029 \| 3.8547 \| 17 \| \| 1.6887 \| 3.8189 \| 18 \| \| 1.6753 \| 3.8725 \| 19 \| \| 1.6622 \| 3.9050 \| 20 \| \| 1.6498 \| 3.9306 \| 21 \| \| 1.6376 \| 3.9670 \| 22 \| \| 1.6262 \| 3.9569 \| 23 \| \| 1.6150 \| 3.9473 \| 24 \| \| 1.6044 \| 3.9695 \| 25 \| \| 1.5943 \| 3.9193 \| 26 \| \| 1.5844 \| 3.9739 \| 27 \| \| 1.5751 \| 4.0273 \| 28 \| \| 1.5660 \| 4.0224 \| 29 \| \| 1.5574 \| 4.0163 \| 30 \| \| 1.5491 \| 4.0466 \| 31 \| \| 1.5413 \| 4.0520 \| 32 \| \| 1.5342 \| 4.0640 \| 33 \| \| 1.5270 \| 4.0616 \| 34 \| \| 1.5199 \| 4.0611 \| 35 \| \| 1.5133 \| 4.0884 \| 36 \| \| 1.5073 \| 4.0827 \| 37 \| \| 1.5015 \| 4.0972 \| 38 \| \| 1.4962 \| 4.0991 \| 39 \| \| 1.4908 \| 4.0989 \| 40 \| \| 1.4858 \| 4.1078 \| 41 \| \| 1.4814 \| 4.1295 \| 42 \| \| 1.4773 \| 4.1142 \| 43 \| \| 1.4730 \| 4.1200 \| 44 \| \| 1.4699 \| 4.1270 \| 45 \| \| 1.4664 \| 4.1425 \| 46 \| \| 1.4637 \| 4.1392 \| 47 \| \| 1.4612 \| 4.1365 \| 48 \| \| 1.4591 \| 4.1433 \| 49 \| ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.0 - Datasets 2.8.0 - Tokenizers 0.13.2
keras-sd/text-encoder-tflite	keras-sd	2023-01-24T14:39:01Z	0	0	keras	[ "keras", "tflite", "text encoder", "stable diffusion", "v1.4", "text-to-image", "license:apache-2.0", "region:us" ]	text-to-image	2022-12-27T00:26:41Z	--- license: apache-2.0 library_name: keras pipeline_tag: text-to-image tags: - text encoder - stable diffusion - v1.4 --- This repository hosts the TFLite version of `text encoder` part of [KerasCV Stable Diffusion](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion). Stable Diffusion consists of `text encoder`, `diffusion model`, `decoder`, and some glue codes to handl inputs and outputs of each part. The TFLite version of `text encoder` in this repository is built not only with the `text encoder` itself but also TensorFlow operations that generates `context` and `unconditional context`. These output should be passed down to the `diffusion model` which is hosted in [this repository](https://huggingface.co/keras-sd/diffusion-model-tflite/tree/main). TFLite conversion was based on the `SavedModel` from [this repository](https://huggingface.co/keras-sd/tfs-text-encoder/tree/main), and TensorFlow version `>= 2.12-nightly` was used. - NOTE: [Dynamic range quantization](https://www.tensorflow.org/lite/performance/post_training_quant#optimizing_an_existing_model) was used. - NOTE: TensorFlow version `< 2.12-nightly` will fail for the conversion process. - NOTE: For those who wonder how `SavedModel` is constructed, find it in [keras-sd-serving repository](https://github.com/deep-diver/keras-sd-serving).
threite/bert-finetuned-ner	threite	2023-01-24T14:32:13Z	5	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-24T13:30:59Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9485085819030161 - name: Recall type: recall value: 0.9579266240323123 - name: F1 type: f1 value: 0.9531943397806245 - name: Accuracy type: accuracy value: 0.9919979751567306 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0176 - Precision: 0.9485 - Recall: 0.9579 - F1: 0.9532 - Accuracy: 0.9920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.003 \| 1.0 \| 1756 \| 0.0180 \| 0.9397 \| 0.9461 \| 0.9429 \| 0.9908 \| \| 0.0013 \| 2.0 \| 3512 \| 0.0163 \| 0.9456 \| 0.9566 \| 0.9511 \| 0.9919 \| \| 0.0006 \| 3.0 \| 5268 \| 0.0176 \| 0.9485 \| 0.9579 \| 0.9532 \| 0.9920 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1 - Datasets 2.7.1 - Tokenizers 0.13.1
IshitaSingh/t5-small-finetuned-xsum	IshitaSingh	2023-01-24T14:28:19Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-23T17:04:49Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: train args: default metrics: - name: Rouge1 type: rouge value: 28.3594 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4785 - Rouge1: 28.3594 - Rouge2: 7.7695 - Rougel: 22.2562 - Rougelsum: 22.262 - Gen Len: 18.8329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.716 \| 1.0 \| 12753 \| 2.4785 \| 28.3594 \| 7.7695 \| 22.2562 \| 22.262 \| 18.8329 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
wooihen/ppo-PyramidsRND1	wooihen	2023-01-24T14:23:50Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-24T14:23:44Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: wooihen/ppo-PyramidsRND1 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
newwater/a2c-PandaReachDense-v2	newwater	2023-01-24T13:30:39Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T13:29:03Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.90 +/- 0.67 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
javiervela/Reinforce-Pixelcopter-PLE-v0	javiervela	2023-01-24T13:27:24Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T13:27:21Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 19.90 +/- 12.01 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Unterwexi/Reinforce-pixelcopterV2	Unterwexi	2023-01-24T13:06:23Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T13:05:53Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopterV2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 90.30 +/- 78.30 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
huggingtweets/btc-eth-vitalikbuterin	huggingtweets	2023-01-24T12:52:32Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-01-24T12:47:16Z	--- language: en thumbnail: http://www.huggingtweets.com/btc-eth-vitalikbuterin/1674564747266/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/977496875887558661/L86xyLF4_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1292159368943693824/JXYCQur0_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1258321209730760705/1hkrHoOT_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">vitalik.eth & BTC Times & ETH Zürich</div> <div style="text-align: center; font-size: 14px;">@btc-eth-vitalikbuterin</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from vitalik.eth & BTC Times & ETH Zürich. \| Data \| vitalik.eth \| BTC Times \| ETH Zürich \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3243 \| 3241 \| 3246 \| \| Retweets \| 241 \| 1215 \| 1023 \| \| Short tweets \| 123 \| 35 \| 34 \| \| Tweets kept \| 2879 \| 1991 \| 2189 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/d3n8pkg2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btc-eth-vitalikbuterin's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/x6co1yfz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/x6co1yfz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/btc-eth-vitalikbuterin') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Schwarzschild009/q-FrozenLake-v1-4x4-noSlippery	Schwarzschild009	2023-01-24T12:44:10Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T12:44:07Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="Schwarzschild009/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
StatsGary/xlm-roberta-base-finetuned-panx-de	StatsGary	2023-01-24T12:36:58Z	7	1	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-01-20T16:20:24Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: train args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8654677896653767 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1405 - F1: 0.8655 ## 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: 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2495 \| 1.0 \| 787 \| 0.1764 \| 0.8184 \| \| 0.1299 \| 2.0 \| 1574 \| 0.1427 \| 0.8562 \| \| 0.0771 \| 3.0 \| 2361 \| 0.1405 \| 0.8655 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
stablemobile/fuatuzumcu	stablemobile	2023-01-24T12:28:55Z	5	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-24T12:27:06Z	--- license: creativeml-openrail-m tags: - text-to-image widget: - text: fuatuzumcu --- ### fuatuzumcu Dreambooth model trained by stablemobile with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You 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). Don't forget to use the concept prompts! Sample pictures of: fuatuzumcu (use that on your prompt) ![fuatuzumcu 0](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%281%29.jpg)![fuatuzumcu 1](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%282%29.jpg)![fuatuzumcu 2](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%283%29.jpg)![fuatuzumcu 3](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%284%29.jpg)![fuatuzumcu 4](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%285%29.jpg)![fuatuzumcu 5](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%286%29.jpg)![fuatuzumcu 6](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%287%29.jpg)![fuatuzumcu 7](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%288%29.jpg)![fuatuzumcu 8](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%289%29.jpg)![fuatuzumcu 9](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%2810%29.jpg)![fuatuzumcu 10](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%2811%29.jpg)![fuatuzumcu 11](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%2812%29.jpg)![fuatuzumcu 12](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%2813%29.jpg)![fuatuzumcu 13](https://huggingface.co/stablemobile/fuatuzumcu/resolve/main/concept_images/fuatuzumcu_%2814%29.jpg)
tim-binding/dqn-SpaceInvadersNoFrameskip-v4	tim-binding	2023-01-24T12:21:39Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T12:21:01Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 597.00 +/- 225.89 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tim-binding -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tim-binding -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tim-binding ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
sprenkamp/distilbert-base-uncased-finetuned-squad	sprenkamp	2023-01-24T12:16:41Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-01-24T09:05:00Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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: 1.1596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.224 \| 1.0 \| 5533 \| 1.1606 \| \| 0.9626 \| 2.0 \| 11066 \| 1.1240 \| \| 0.7619 \| 3.0 \| 16599 \| 1.1596 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
huggingtweets/btc-doveywan-eth	huggingtweets	2023-01-24T12:08:10Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-01-24T11:44:34Z	--- language: en thumbnail: http://www.huggingtweets.com/btc-doveywan-eth/1674562085261/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1616618733556101124/oXxgxm8O_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1292159368943693824/JXYCQur0_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1258321209730760705/1hkrHoOT_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Dovey "Rug The Fiat" Wan & BTC Times & ETH Zürich</div> <div style="text-align: center; font-size: 14px;">@btc-doveywan-eth</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Dovey "Rug The Fiat" Wan & BTC Times & ETH Zürich. \| Data \| Dovey "Rug The Fiat" Wan \| BTC Times \| ETH Zürich \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3244 \| 3241 \| 3246 \| \| Retweets \| 311 \| 1215 \| 1023 \| \| Short tweets \| 264 \| 35 \| 34 \| \| Tweets kept \| 2669 \| 1991 \| 2189 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/fjov15tq/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @btc-doveywan-eth's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/n69s58ct) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/n69s58ct/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/btc-doveywan-eth') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
AmirMesbah/q-Taxi-v3	AmirMesbah	2023-01-24T12:03:14Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T12:03:12Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="AmirMesbah/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
stevaras2/dqn-SpaceInvadersNoFrameskip-v4	stevaras2	2023-01-24T11:48:26Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T11:47:49Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 569.50 +/- 94.77 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga stevaras2 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga stevaras2 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga stevaras2 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
MrFitzmaurice/roberta-finetuned-topic-5	MrFitzmaurice	2023-01-24T11:35:27Z	0	0	adapter-transformers	[ "adapter-transformers", "pytorch", "tensorboard", "roberta", "code", "NLP", "text-classification", "en", "region:us" ]	text-classification	2023-01-12T08:07:19Z	--- language: - en metrics: - accuracy library_name: adapter-transformers pipeline_tag: text-classification tags: - code - NLP ---
jfiekdjdk/gpt2-furry-prompt-gen	jfiekdjdk	2023-01-24T11:25:22Z	4	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-01-23T15:26:16Z	### WARNING: this model is overfitting!!!
maneprajakta/m2m100_418M-test	maneprajakta	2023-01-24T11:15:01Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "generated_from_trainer", "dataset:dataset", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-24T08:49:56Z	--- license: mit tags: - generated_from_trainer datasets: - dataset metrics: - bleu model-index: - name: m2m100_418M-test results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: dataset type: dataset config: uptycs split: train args: uptycs metrics: - name: Bleu type: bleu value: 29.9962 --- <!-- 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. --> # m2m100_418M-test This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.9953 - Bleu: 29.9962 - Gen Len: 41.6441 ## 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: 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 0.3804 \| 1.0 \| 1267 \| 0.9953 \| 29.9962 \| 41.6441 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2
newwater/ppo-SnowballTarget	newwater	2023-01-24T10:58:20Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-01-24T10:58:13Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: newwater/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
csebuetnlp/banglabert_small	csebuetnlp	2023-01-24T10:49:22Z	75	2	transformers	[ "transformers", "pytorch", "electra", "pretraining", "bn", "endpoints_compatible", "region:us" ]	null	2023-01-24T10:01:21Z	--- language: - bn licenses: - cc-by-nc-sa-4.0 --- # BanglaBERT (small) This repository contains the pretrained discriminator checkpoint of the model BanglaBERT (small). This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali. For finetuning on different downstream tasks such as `Sentiment classification`, `Named Entity Recognition`, `Natural Language Inference` etc., refer to the scripts in the official GitHub [repository](https://github.com/csebuetnlp/banglabert). Note: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). All finetuning scripts in the official GitHub repository uses this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below: ## Using this model as a discriminator in `transformers` (tested on 4.11.0.dev0) ```python from transformers import AutoModelForPreTraining, AutoTokenizer from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer import torch model = AutoModelForPreTraining.from_pretrained("csebuetnlp/banglabert_small") tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglabert_small") original_sentence = "আমি কৃতজ্ঞ কারণ আপনি আমার জন্য অনেক কিছু করেছেন।" fake_sentence = "আমি হতাশ কারণ আপনি আমার জন্য অনেক কিছু করেছেন।" fake_sentence = normalize(fake_sentence) # this normalization step is required before tokenizing the text fake_tokens = tokenizer.tokenize(fake_sentence) fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") discriminator_outputs = model(fake_inputs).logits predictions = torch.round((torch.sign(discriminator_outputs) + 1) / 2) [print("%7s" % token, end="") for token in fake_tokens] print("\n" + "-" * 50) [print("%7s" % int(prediction), end="") for prediction in predictions.squeeze().tolist()[1:-1]] print("\n" + "-" * 50) ``` ## Benchmarks * Zero-shot cross-lingual transfer-learning \| Model \| Params \| SC (macro-F1) \| NLI (accuracy) \| NER (micro-F1) \| QA (EM/F1) \| BangLUE score \| \|----------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\| \|[mBERT](https://huggingface.co/bert-base-multilingual-cased) \| 180M \| 27.05 \| 62.22 \| 39.27 \| 59.01/64.18 \| 50.35 \| \|[XLM-R (base)](https://huggingface.co/xlm-roberta-base) \| 270M \| 42.03 \| 72.18 \| 45.37 \| 55.03/61.83 \| 55.29 \| \|[XLM-R (large)](https://huggingface.co/xlm-roberta-large) \| 550M \| 49.49 \| 78.13 \| 56.48 \| 71.13/77.70 \| 66.59 \| \|[BanglishBERT](https://huggingface.co/csebuetnlp/banglishbert) \| 110M \| 48.39 \| 75.26 \| 55.56 \| 72.87/78.63 \| 66.14 \| * Supervised fine-tuning \| Model \| Params \| SC (macro-F1) \| NLI (accuracy) \| NER (micro-F1) \| QA (EM/F1) \| BangLUE score \| \|----------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------\| \|[mBERT](https://huggingface.co/bert-base-multilingual-cased) \| 180M \| 67.59 \| 75.13 \| 68.97 \| 67.12/72.64 \| 70.29 \| \|[XLM-R (base)](https://huggingface.co/xlm-roberta-base) \| 270M \| 69.54 \| 78.46 \| 73.32 \| 68.09/74.27 \| 72.82 \| \|[XLM-R (large)](https://huggingface.co/xlm-roberta-large) \| 550M \| 70.97 \| 82.40 \| 78.39 \| 73.15/79.06 \| 76.79 \| \|[sahajBERT](https://huggingface.co/neuropark/sahajBERT) \| 18M \| 71.12 \| 76.92 \| 70.94 \| 65.48/70.69 \| 71.03 \| \|[BanglishBERT](https://huggingface.co/csebuetnlp/banglishbert) \| 110M \| 70.61 \| 80.95 \| 76.28 \| 72.43/78.40 \| 75.73 \| \|[BanglaBERT (small)](https://huggingface.co/csebuetnlp/banglabert_small) \| 13M \| 69.29 \| 76.75 \| 73.41 \| 63.30/69.65 \| 70.38 \| \|[BanglaBERT](https://huggingface.co/csebuetnlp/banglabert) \| 110M \| 72.89 \| 82.80 \| 77.78 \| 72.63/79.34 \| 77.09 \| \|[BanglaBERT (large)](https://huggingface.co/csebuetnlp/banglabert_large) \| 335M \| 71.94 \| 83.41 \| 79.20 \| 76.10/81.50 \| 78.43 \| The benchmarking datasets are as follows: * SC: [Sentiment Classification](https://aclanthology.org/2021.findings-emnlp.278) * NER: [Named Entity Recognition](https://multiconer.github.io/competition) * NLI: [Natural Language Inference](https://github.com/csebuetnlp/banglabert/#datasets) * QA: [Question Answering](https://github.com/csebuetnlp/banglabert/#datasets) ## Citation If you use this model, please cite the following paper: ``` @inproceedings{bhattacharjee-etal-2022-banglabert, title = "{B}angla{BERT}: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in {B}angla", author = "Bhattacharjee, Abhik and Hasan, Tahmid and Ahmad, Wasi and Mubasshir, Kazi Samin and Islam, Md Saiful and Iqbal, Anindya and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.findings-naacl.98", pages = "1318--1327", abstract = "In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed {`}Bangla2B+{'}) by crawling 110 popular Bangla sites. We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction. In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB). BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models. We are making the models, datasets, and a leaderboard publicly available at \url{https://github.com/csebuetnlp/banglabert} to advance Bangla NLP.", } ``` If you use the normalization module, please cite the following paper: ``` @inproceedings{hasan-etal-2020-low, title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation", author = "Hasan, Tahmid and Bhattacharjee, Abhik and Samin, Kazi and Hasan, Masum and Basak, Madhusudan and Rahman, M. Sohel and Shahriyar, Rifat", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.207", doi = "10.18653/v1/2020.emnlp-main.207", pages = "2612--2623", abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.", } ```
morit/xlm-t-roberta-base-mnli-xnli	morit	2023-01-24T10:42:43Z	5	2	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "ar", "bg", "de", "el", "en", "es", "tr", "th", "ur", "hi", "zh", "vi", "fr", "ru", "sw", "dataset:xnli", "dataset:multi_nli", "arxiv:1911.02116", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-24T09:11:51Z	--- license: mit datasets: - xnli - multi_nli language: - ar - bg - de - el - en - es - tr - th - ur - hi - zh - vi - fr - ru - sw metrics: - accuracy --- # XLM-T-ROBERTA-BASE-MNLI-XNLI ## Model description This model takes the XLM-Roberta-base model which has been continued to pre-traine on a large corpus of Twitter in multiple languages. It was developed following a similar strategy as introduced as part of the [Tweet Eval](https://github.com/cardiffnlp/tweeteval) framework. The model is further finetuned on the MNLI dataset and also on the xnli dataset. ## Intended Usage This model was developed to do Zero-Shot Text Classification in the realm of Hate Speech Detection. It is finetuned on the whole xnli train set containing 15 different languages like: ar, bg ,de , en, el , es, fr, hi, ru, sw, th, tr, ur, vi, zh Since the base model was pre-trained on 100 different languages it has shown some effectiveness in other languages. Please refer to the list of languages in the [XLM Roberta paper](https://arxiv.org/abs/1911.02116) ### Usage with Zero-Shot Classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="morit/xlm-t-roberta-base-mnli-xnli") ``` ## Training This model was pre-trained on set of 100 languages, as described in the original paper. It was then fine-tuned on the task of NLI on the concatenated MNLI train set. Finally, it was trained for one additional epoch on only XNLI data where the translations for the premise and hypothesis are shuffled such that the premise and hypothesis for each example come from the same original English example but the premise and hypothesis are of different languages. The following hyper-parameters were chosen: - learning rate: 2e-5 - batch size: 32 - max sequence: length 128 using one GPU (NVIDIA GeForce RTX 3090) ## Evaluation The model was evaluated on all the test sets of the xnli dataset resulting in the following accuracies: \| ar \| bg \| de \| el \| en \| es \| fr \| hi\| ru \| sw \| th \| tr \|ur \| vi \| zh \| \|-----\|-----\|-----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\| \|0.776\|0.804\|0.796\|0.791\|0.851\|0.813\|0.806\|0.757\|0.783\|0.716\|0.765\|0.780\|0.705\|0.795\|0.782\|
lewtun/dummy-trl-model	lewtun	2023-01-24T10:42:37Z	4	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-01-24T10:42:24Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="lewtun/dummy-trl-model") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("lewtun/dummy-trl-model") model = AutoModelForCausalLMWithValueHead.from_pretrained("lewtun/dummy-trl-model") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
summervent/russian-spellchecking	summervent	2023-01-24T10:40:07Z	3	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-22T10:56:20Z	--- tags: - generated_from_trainer model-index: - name: russian-spellchecking results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # russian-spellchecking This model is a fine-tuned version of [UrukHan/t5-russian-spell](https://huggingface.co/UrukHan/t5-russian-spell) on an unknown dataset. ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu116 - Tokenizers 0.13.2
ludsil/taxi	ludsil	2023-01-24T10:38:49Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T10:38:46Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.62 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ludsil/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ludsil/q-FrozenLake-v1-4x4-noSlippery	ludsil	2023-01-24T10:31:17Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T10:31:14Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ludsil/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
kostasang/ppo-Pyramids	kostasang	2023-01-24T10:24:33Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-01-24T10:24:25Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: kostasang/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
fedihch/InvoiceReceiptClassifier_LayoutLMv3	fedihch	2023-01-24T10:06:30Z	51	1	transformers	[ "transformers", "pytorch", "layoutlmv3", "feature-extraction", "image-classification", "es", "en", "multilingual", "license:other", "endpoints_compatible", "region:us" ]	image-classification	2022-08-16T12:58:41Z	--- language: - es - en - multilingual license: other tags: - image-classification pipeline_tag: image-classification widget: - src: https://upserve.com/media/sites/2/Bill-from-Mezcalero-in-Washington-D.C.-photo-by-Alfredo-Solis-1-e1507226752437.jpg example_title: receipt - src: https://templates.invoicehome.com/invoice-template-us-neat-750px.png example_title: invoice --- InvoiceReceiptClassifier_LayoutLMv3 is a fine-tuned LayoutLMv3 model that classifies a document to an invoice or receipt. ## Quick start: using the raw model ```python from transformers import ( AutoModelForSequenceClassification, AutoProcessor, ) from PIL import Image from urllib.request import urlopen model = AutoModelForSequenceClassification.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") processor = AutoProcessor.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") input_img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/ReceiptSwiss.jpg/1024px-ReceiptSwiss.jpg" with urlopen(input_img_url) as testImage: input_img = Image.open(testImage).convert("RGB") encoded_inputs = processor(input_img, padding="max_length", return_tensors="pt") outputs = model(**encoded_inputs) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() id2label = {0: "invoice", 1: "receipt"} print(id2label[predicted_class_idx]) ```
jwright94/dqn-SpaceInvadersNoFrameskip-v4	jwright94	2023-01-24T09:52:17Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T08:45:50Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 449.00 +/- 125.50 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jwright94 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga jwright94 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga jwright94 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 75000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
kostasang/ppo-SnowballTarget	kostasang	2023-01-24T09:48:58Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-01-24T09:48:50Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: kostasang/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
morit/XLM-T-full-xnli	morit	2023-01-24T09:41:27Z	21	2	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "zero-shot-classification", "de", "dataset:xnli", "arxiv:1911.02116", "arxiv:2104.12250", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	zero-shot-classification	2023-01-19T12:15:38Z	--- license: mit datasets: - xnli language: - de metrics: - accuracy pipeline_tag: zero-shot-classification --- # XLM-ROBERTA-BASE-XNLI ## Model description This model takes the XLM-Roberta-base model which has been continued to pre-traine on a large corpus of Twitter in multiple languages. It was developed following a similar strategy as introduced as part of the [Tweet Eval](https://github.com/cardiffnlp/tweeteval) framework. The model is further finetuned on all of the languages of the XNLI train set ## Intended Usage This model was developed to do Zero-Shot Text Classification in the realm of Hate Speech Detection. It is finetuned on the whole xnli train set containing 15 different languages like: ar, bg ,de , en, el , es, fr, hi, ru, sw, th, tr, ur, vi, zh Since the base model was pre-trained on 100 different languages it has shown some effectiveness in other languages. Please refer to the list of languages in the [XLM Roberta paper](https://arxiv.org/abs/1911.02116) ### Usage with Zero-Shot Classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="morit/XLM-T-full-xnli") ``` ## Training This model was pre-trained on a set of 100 languages and follwed further training on 198M multilingual tweets as described in the original [paper](https://arxiv.org/abs/2104.12250). Further it was trained on the full train set of XNLI dataset which is a machine translated version of the MNLI dataset. It was trained on 5 epochs of the XNLI train set and evaluated on the XNLI eval dataset at the end of every epoch to find the best performing model. The model which had the highest accuracy on the eval set was chosen at the end. ![Training Charts from wandb](screen_wandb.png) - learning rate: 2e-5 - batch size: 32 - max sequence: length 128 using a GPU (NVIDIA GeForce RTX 3090) # Evaluation The model was evaluated on all the test sets of the xnli dataset resulting in the following accuracies: \| ar \| bg \| de \| el \| en \| es \| fr \| hi\| ru \| sw \| th \| tr \|ur \| vi \| zh \| \|-----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\|----\| \| 0.749 \| 0.787 \| 0.774 \| 0.774 \| 0.831 \| 0.796 \| 0.785 \| 0.734 \| 0.761 \| 0.701 \| 0.757 \| 0.758 \| 0.704 \| 0.778 \| 0.774 \|
NotoriousYang/Mybot	NotoriousYang	2023-01-24T09:04:44Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-01-24T09:04:44Z	--- license: creativeml-openrail-m ---
sayakpaul/mit-b0-finetuned-pets	sayakpaul	2023-01-24T08:55:19Z	8	1	keras	[ "keras", "tf", "segformer", "vision", "image-segmentation", "dataset:oxford_pets", "license:other", "region:us" ]	image-segmentation	2023-01-24T08:19:53Z	--- license: other tags: - vision - image-segmentation datasets: - oxford_pets widget: - src: >- https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg example_title: Dog - src: >- https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg example_title: Cat library_name: keras --- # sayakpaul/mit-b0-finetuned-pets This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the [Oxford Pets](https://www.robots.ox.ac.uk/~vgg/data/pets/) dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1481 - Validation Loss: 0.1962 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 6e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.2821 \| 0.2146 \| 0 \| \| 0.2090 \| 0.1983 \| 1 \| \| 0.1920 \| 0.2002 \| 2 \| \| 0.1805 \| 0.1868 \| 3 \| \| 0.1716 \| 0.1920 \| 4 \| \| 0.1651 \| 0.1850 \| 5 \| \| 0.1537 \| 0.1943 \| 6 \| \| 0.1570 \| 0.1842 \| 7 \| \| 0.1462 \| 0.1833 \| 8 \| \| 0.1481 \| 0.1962 \| 9 \| ### Framework versions - Transformers 4.25.1 - TensorFlow 2.10.1 - Tokenizers 0.13.2

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