davanstrien/model_cards_with_readmes · Datasets at Hugging Face

cafeai/cafe-instagram-sd-1-5-v6

cafeai

null

4

0

null

62

null

false

agpl-3.0

null

0

[]

false

true

1,645

false

# Cafe Instagram Unofficial Test v2 This is a test model created to assess the Waifu Diffusion training code, and not intended to be a full-featured or official release. This model has been trained from `runwayml/stable-diffusion-v1-5` for approximately 1.6 epochs on 1.2m images total from various Instagram accounts (primarily Japanese). As the model is undertrained, its performance is marginal. Mixing the model is recommended for better performance. Natural language descriptions (using BLIP), as well as [booru tags](https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger) have been used to assist in captioning. Any Instagram hashtags were also included in the caption data. *Note: Training was done using various aspect ratios, with a base resolution of 768x768, as well as the penultimate CLIP layer. Clip skip of 2 and a resolution of 768x768 or higher is recommended for generations.* ![Example](https://huggingface.co/cafeai/cafe-instagram-sd-1-5-v6/resolve/main/example.jpg) Example: ``` waifu, instagram, cute girl, japaneseidol, idol, アイドル, 自撮り女子, photorealistic, photo, 可愛い, kawaii, cute, gravure, fashion, 1girl, solo, cleavage, cowboy shot Negative prompt: (((mutated hands and fingers))), ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed face))), ((ugly)), ((bad anatomy)), (((bad proportions))), (((extra limbs))), extra face, ((double head)), ((extra head)), (big breast), (((extra feet))), monster, (text), (logo), (blurry), text, english text, watermark, logo, (((anime))) ``` This model is released under the aGPL. You can use this for whatever you like. If you make changes, share them.

4563c92c96812209b1bc54e465375264

JiachengLi/uctopic-base

JiachengLi

luke

10

128

transformers

0

null

true

false

mit

null

0

[]

false

true

16,354

false

# UCTopic This repository contains the code of model UCTopic and an easy-to-use tool UCTopicTool used for Topic Mining, Unsupervised Aspect Extractioin or Phrase Retrieval. Our ACL 2022 paper [UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining](https://arxiv.org/abs/2202.13469). # Quick Links - [Overview](#overview) - [Pretrained Model](#pretrained-model) - [Getting Started](#getting-started) - [UCTopic Model](#uctopic-model) - [UCTopicTool](#uctopictool) - [Experiments in Paper](#experiments) - [Requirements](#requirements) - [Datasets](#datasets) - [Entity Clustering](#entity-clustering) - [Topic Mining](#topic-mining) - [Pretraining](#pretraining) - [Contact](#contact) - [Citation](#citation) # Overview We propose UCTopic, a novel unsupervised contrastive learning framework for context-aware phrase representations and topic mining. UCTopic is pretrained in a large scale to distinguish if the contexts of two phrase mentions have the same semantics. The key to pretraining is positive pair construction from our phrase-oriented assumptions. However, we find traditional in-batch negatives cause performance decay when finetuning on a dataset with small topic numbers. Hence, we propose cluster-assisted contrastive learning(CCL) which largely reduces noisy negatives by selecting negatives from clusters and further improves phrase representations for topics accordingly. # Pretrained Model Our released model: | Model | Note| |:-------------------------------|------| |[uctopic-base](https://drive.google.com/file/d/1XQzi4E9ctdI373CK5O-pXQyBvOONssp1/view?usp=sharing)| Pretrained UCTopic model based on [LUKE-BASE](https://arxiv.org/abs/2010.01057) Unzip to get `uctopic-base` folder. # Getting Started We provide an easy-to-use phrase representation tool based on our UCTopic model. To use the tool, first install the uctopic package from PyPI ```bash pip install uctopic ``` Or directly install it from our code ```bash python setup.py install ``` ## UCTopic Model After installing the package, you can load our model by just two lines of code ```python from uctopic import UCTopic model = UCTopic.from_pretrained('JiachengLi/uctopic-base') ``` The model will automatically download pre-trained parameters from [HuggingFace's models](https://huggingface.co/models). If you encounter any problem when directly loading the models by HuggingFace's API, you can also download the models manually from the above table and use `model = UCTopic.from_pretrained({PATH TO THE DOWNLOAD MODEL})`. To get pre-trained phrase representations, our model inputs are same as [LUKE](https://huggingface.co/docs/transformers/model_doc/luke). Note: please input only ONE span each time, otherwise, will have performance decay according to our empirical results. ```python from uctopic import UCTopicTokenizer, UCTopic tokenizer = UCTopicTokenizer.from_pretrained('JiachengLi/uctopic-base') model = UCTopic.from_pretrained('JiachengLi/uctopic-base') text = "Beyoncé lives in Los Angeles." entity_spans = [(17, 28)] # character-based entity span corresponding to "Los Angeles" inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt") outputs, phrase_repr = model(**inputs) ``` `phrase_repr` is the phrase embedding (size `[768]`) of the phrase `Los Angeles`. `outputs` has the same format as the outputs from `LUKE`. ## UCTopicTool We provide a tool `UCTopicTool` built on `UCTopic` for efficient phrase encoding, topic mining (or unsupervised aspect extraction) or phrase retrieval. ### Initialization `UCTopicTool` is initialized by giving the `model_name_or_path` and `device`. ```python from uctopic import UCTopicTool topic_tool = UCTopicTool('JiachengLi/uctopic-base', device='cuda:0') ``` ### Phrase Encoding Phrases are encoded by our method `UCTopicTool.encode` in batches, which is more efficient than `UCTopic`. ```python phrases = [["This place is so much bigger than others!", (0, 10)], ["It was totally packed and loud.", (15, 21)], ["Service was on the slower side.", (0, 7)], ["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)], ["The ingredient weren't really fresh.", (4, 14)]] embeddings = topic_tool.encode(phrases) # len(embeddings) is equal to len(phrases) ``` **Note**: Each instance in `phrases` contains only one sentence and one span (character-level position) in format `[sentence, span]`. Arguments for `UCTopicTool.encode` are as follows, * **phrase** (List) - A list of `[sentence, span]` to be encoded. * **return_numpy** (bool, *optional*, defaults to `False`) - Return `numpy.array` or `torch.Tensor`. * **normalize_to_unit** (bool, *optional*, defaults to `True`) - Normalize all embeddings to unit vectors. * **keepdim** (bool, *optional*, defaults to `True`) - Keep dimension size `[instance_number, hidden_size]`. * **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch in the model. ### Topic Mining and Unsupervised Aspect Extraction The method `UCTopicTool.topic_mining` can mine topical phrases or conduct aspect extraction from sentences with or without spans. ```python sentences = ["This place is so much bigger than others!", "It was totally packed and loud.", "Service was on the slower side.", "I ordered 2 mojitos: 1 lime and 1 mango.", "The ingredient weren't really fresh."] spans = [[(0, 10)], # This place [(15, 21), (26, 30)], # packed; loud [(0, 7)], # Service [(12, 19), (21, 27), (32, 39)], # mojitos; 1 lime; 1 mango [(4, 14)]] # ingredient # len(sentences) is equal to len(spans) output_data, topic_phrase_dict = tool.topic_mining(sentences, spans, \ n_clusters=[15, 25]) # predict topic for new phrases phrases = [["The food here is amazing!", (4, 8)], ["Lovely ambiance with live music!", (21, 31)]] topics = tool.predict_topic(phrases) ``` **Note**: If `spans` is not given, `UCTopicTool` will extract noun phrases by [spaCy](https://spacy.io/). Arguments for `UCTopicTool.topic_mining` are as follows, Data arguments: * **sentences** (List) - A List of sentences for topic mining. * **spans** (List, *optional*, defaults to `None`) - A list of span list corresponding sentences, e.g., `[[(0, 9), (5, 7)], [(1, 2)]]` and `len(sentences)==len(spans)`. If None, automatically mine phrases from noun chunks. Clustering arguments: * **n_clusters** (int or List, *optional*, defaults to `2`) - The number of topics. When `n_clusters` is a list, `n_clusters[0]` and `n_clusters[1]` will be the minimum and maximum numbers to search, `n_clusters[2]` is the search step length (if not provided, default to 1). * **meric** (str, *optional*, defaults to `"cosine"`) - The metric to measure the distance between vectors. `"cosine"` or `"euclidean"`. * **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch for phrase encoding. * **max_iter** (int, *optional*, defaults to `300`) - The maximum iteration number of kmeans. CCL-finetune arguments: * **ccl_finetune** (bool, *optional*, defaults to `True`) - Whether to conduct CCL-finetuning in the paper. * **batch_size_finetune** (int, *optional*, defaults to `8`) - The size of mini-batch for finetuning. * **max_finetune_num** (int, *optional*, defaults to `100000`) - The maximum number of training instances for finetuning. * **finetune_step** (int, *optional*, defaults to `2000`) - The number of training steps for finetuning. * **contrastive_num** (int, *optional*, defaults to `5`) - The number of negatives in contrastive learning. * **positive_ratio** (float, *optional*, defaults to `0.1`) - The ratio of the most confident instances for finetuning. * **n_sampling** (int, *optional*, defaults to `10000`) - The number of sampled examples for cluster number confirmation and finetuning. Set to `-1` to use the whole dataset. * **n_workers** (int, *optional*, defaults to `8`) - The number of workers for preprocessing data. Returns for `UCTopicTool.topic_mining` are as follows, * **output_data** (List) - A list of sentences and corresponding phrases and topic numbers. Each element is `[sentence, [[start1, end1, topic1], [start2, end2, topic2]]]`. * **topic_phrase_dict** (Dict) - A dictionary of topics and the list of phrases under a topic. The phrases are sorted by their confidence scores. E.g., `{topic: [[phrase1, score1], [phrase2, score2]]}`. The method `UCTopicTool.predict_topic` predicts the topic ids for new phrases based on your training results from `UCTopicTool.topic_mining`. The inputs of `UCTopicTool.predict_topic` are same as `UCTopicTool.encode` and returns a list of topic ids (int). ### Phrase Similarities and Retrieval The method `UCTopicTool.similarity` compute the cosine similarities between two groups of phrases: ```python phrases_a = [["This place is so much bigger than others!", (0, 10)], ["It was totally packed and loud.", (15, 21)]] phrases_b = [["Service was on the slower side.", (0, 7)], ["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)], ["The ingredient weren't really fresh.", (4, 14)]] similarities = tool.similarity(phrases_a, phrases_b) ``` Arguments for `UCTopicTool.similarity` are as follows, * **queries** (List) - A list of `[sentence, span]` as queries. * **keys** (List or `numpy.array`) - A list of `[sentence, span]` as keys or phrase representations (`numpy.array`) from `UCTopicTool.encode`. * **batch_size** (int, *optional*, defaults to `64`) - The size of mini-batch in the model. `UCTopicTool.similarity` returns a `numpy.array` contains the similarities between phrase pairs in two groups. The methods `UCTopicTool.build_index` and `UCTopicTool.search` are used for phrase retrieval: ```python phrases = [["This place is so much bigger than others!", (0, 10)], ["It was totally packed and loud.", (15, 21)], ["Service was on the slower side.", (0, 7)], ["I ordered 2 mojitos: 1 lime and 1 mango.", (12, 19)], ["The ingredient weren't really fresh.", (4, 14)]] # query multiple phrases query1 = [["The food here is amazing!", (4, 8)], ["Lovely ambiance with live music!", (21, 31)]] # query single phrases query2 = ["The food here is amazing!", (4, 8)] tool.build_index(phrases) results = tool.search(query1, top_k=3) # or results = tool.search(query2, top_k=3) ``` We also support [faiss](https://github.com/facebookresearch/faiss), an efficient similarity search library. Just install the package following [instructions](https://github.com/facebookresearch/faiss/blob/main/INSTALL.md) here and `UCTopicTool` will automatically use `faiss` for efficient search. `UCTopicTool.search` returns the ranked top k phrases for each query. ### Save and Load finetuned UCTopicTool The methods `UCTopicTool.save` and `UCTopicTool.load` are used for save and load all paramters of `UCTopicTool`. Save: ```python tool = UCTopicTool('JiachengLi/uctopic-base', 'cuda:0') # finetune UCTopic with CCL output_data, topic_phrase_dict = tool.topic_mining(sentences, spans, \ n_clusters=[15, 25]) tool.save(**your directory**) ``` Load: ```python tool = UCTopicTool('JiachengLi/uctopic-base', 'cuda:0') tool.load(**your directory**) ``` The loaded parameters will be used for all methods (for encoding, topic mining, phrase similarities and retrieval) introduced above. # Experiments In this section, we re-implement experiments in our paper. ## Requirements First, install PyTorch by following the instructions from [the official website](https://pytorch.org). To faithfully reproduce our results, please use the correct `1.9.0` version corresponding to your platforms/CUDA versions. Then run the following script to install the remaining dependencies, ```bash pip install -r requirements.txt ``` Download `en_core_web_sm` model from spacy, ```bash python -m spacy download en_core_web_sm ``` ## Datasets The downstream datasets used in our experiments can be downloaded from [here](https://drive.google.com/file/d/1dVIp9li1Wdh0JgU8slsWm0ObcitbQtSL/view?usp=sharing). ## Entity Clustering The config file of entity clustering is `clustering/consts.py` and most arguments are self-explained. Please setup `--gpu` and `--data_path` before running. The clustering scores will be printed. Clustering with our pre-trained phrase embeddings. ```bash python clustering.py --gpu 0 ``` Clustering with our pre-trained phrase embeddings and Cluster-Assisted Constrastive Learning (CCL) proposed in our paper. ```bash python clustering_ccl_finetune.py --gpu 0 ``` ## Topic Mining The config file of entity clustering is `topic_modeling/consts.py`. **Key Argument Table** | Arguments | Description | |:-----------------|:-----------:| | --num_classes |**Min** and **Max** number of classes, e.g., `[5, 15]`. Our model will find the class number by [silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html).| | --sample_num_cluster |Number of sampled phrases to confirm class number.| | --sample_num_finetune|Number of sampled phrases for CCL finetuning.| | --contrastive_num|Number of negative classes for CCL finetuning.| | --finetune_step | CCL finetuning steps (maximum global steps for finetuning).| **Tips**: Please tune `--batch_size` or `--contrastive_num` for suitable GPU memory usage. Topic mining with our pre-trained phrase embeddings and Cluster-Assisted Constrastive Learning (CCL) proposed in our paper. ```bash python find_topic.py --gpu 0 ``` **Outputs** We output three files under `topic_results`: | File Name | Description | |:-----------------|:-----------:| | `merged_phraes_pred_prob.pickle` |A dictionary of phrases and their topic number and prediction probability. A topic of a phrase is merged from all phrase mentioins. `{phrase: [topic_id, probability]}`, e.g., {'fair prices': [0, 0.34889686]}| | `phrase_instances_pred.json`| A list of all mined phrase mentions. Each element is `[[doc_id, start, end, phrase_mention], topic_id]`.| | `topics_phrases.json`|A dictionary of topics and corresponding phrases sorted by probability. `{'topic_id': [[phrase1, prob1], [phrase2, prob2]]}`| ### Pretraining **Data** For unsupervised pretraining of UCTopic, we use article and span with links from English Wikipedia and Wikidata. Our processed dataset can be downloaded from [here](https://drive.google.com/file/d/1wflsmhPI9J0ZA6aVRl2mQjHIE6JIvzAv/view?usp=sharing). **Training scripts** We provide example training scripts and our default training parameters for unsupervised training of UCTopic in `run_example.sh`. ```bash bash run_example.sh ``` Arguments description can be found in `pretrain.py`. All the other arguments are standard Huggingface's `transformers` training arguments. **Convert models** Our pretrained checkpoints are slightly different from the checkpoint `uctopic-base`. Please refer `convert_uctopic_parameters.py` to convert it. # Contact If you have any questions related to the code or the paper, feel free to email Jiacheng (`[email protected]`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker! # Citation Please cite our paper if you use UCTopic in your work: ```bibtex @inproceedings{Li2022UCTopicUC, title = "{UCT}opic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining", author = "Li, Jiacheng and Shang, Jingbo and McAuley, Julian", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.acl-long.426", doi = "10.18653/v1/2022.acl-long.426", pages = "6159--6169" } ```

31a0b0f6a1d0e82a610d66a2e89765ca

orhanxakarsu/turkishPoe-generation-1

orhanxakarsu

gpt2

9

2

transformers

0

text-generation

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,509

false

# orhanxakarsu/turkishPoe-generation-1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.7319 - Validation Loss: 5.8020 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 12731, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.003} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 6.7319 | 5.8020 | 0 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

3913c6fc936a4cc0e5b18c5fd4ef8923

jonatasgrosman/exp_w2v2t_zh-cn_wavlm_s368

jonatasgrosman

wavlm

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['zh-CN']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'zh-CN']

false

true

445

false

# exp_w2v2t_zh-cn_wavlm_s368 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

d230f167fb11f4438cfacd741ba47580

Tanhim/translation-En2De

Tanhim

marian

15

14

transformers

3

translation

true

false

gpl

['de']

['wmt19']

null

1

0

['translation']

false

true

624

false

<h2> English to German Translation </h2> Model Name: Tanhim/translation-En2De language: German or Deutsch thumbnail: https://huggingface.co/Tanhim/translation-En2De ### How to use You can use this model directly with a pipeline for machine translation. Since the generation relies on some randomness, I set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> text_En2De= pipeline('translation', model='Tanhim/translation-En2De', tokenizer='Tanhim/translation-En2De') >>> set_seed(42) >>> text_En2De("My name is Karl and I live in Aachen") ``` ### beta version

09c78446ba2455bbadeda97b724e0aae

mrm8488/flan-t5-xl-finetuned-gsm8k

mrm8488

t5

13

8

transformers

0

text2text-generation

true

false

apache-2.0

null

['gsm8k']

null

0

['generated_from_trainer']

true

1,364

false

# flan-t5-xl-finetuned-gsm8k This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the gsm8k dataset. It achieves the following results on the evaluation set: - Loss: 0.2853 ## 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: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2845 | 1.0 | 1868 | 0.2778 | | 0.2204 | 2.0 | 3736 | 0.2718 | | 0.1803 | 3.0 | 5604 | 0.2762 | | 0.1578 | 4.0 | 7472 | 0.2853 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

e13a4d284b4e2ac62ee601dc35a91b1f

taqwa92/whisper-small-ArabicT11

taqwa92

whisper

16

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['taqwa92/tm_data']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,288

false

# Whisper Small Arabic- Taqwa This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the tm_data dataset. It achieves the following results on the evaluation set: - Loss: 0.5306 - Wer: 46.4256 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2375 | 4.85 | 500 | 0.5306 | 46.4256 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

0b49e8bc46ed974aeecaae8af778b115

gokuls/distilbert_add_GLUE_Experiment_logit_kd_wnli_384

gokuls

distilbert

17

3

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,814

false

# distilbert_add_GLUE_Experiment_logit_kd_wnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3434 - Accuracy: 0.5634 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.368 | 1.0 | 3 | 0.3481 | 0.4366 | | 0.3551 | 2.0 | 6 | 0.3499 | 0.4366 | | 0.3472 | 3.0 | 9 | 0.3441 | 0.5634 | | 0.3518 | 4.0 | 12 | 0.3434 | 0.5634 | | 0.3492 | 5.0 | 15 | 0.3494 | 0.4366 | | 0.3495 | 6.0 | 18 | 0.3481 | 0.4366 | | 0.3495 | 7.0 | 21 | 0.3440 | 0.5634 | | 0.3463 | 8.0 | 24 | 0.3437 | 0.5634 | | 0.349 | 9.0 | 27 | 0.3444 | 0.5634 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

fe52272d6697cdf5bfb63fbdef80cbfd

huxxx657/roberta-base-finetuned-squad

huxxx657

roberta

23

6

transformers

0

question-answering

true

false

mit

null

['squad_v2']

null

0

['generated_from_trainer']

true

1,147

false

# roberta-base-finetuned-squad This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.8152 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8557 | 1.0 | 8239 | 0.8152 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

904904c5c4249a286115495afd83e435

KoichiYasuoka/deberta-large-japanese-aozora

KoichiYasuoka

deberta-v2

8

6

transformers

5

fill-mask

true

false

cc-by-sa-4.0

['ja']

null

0

['japanese', 'masked-lm']

false

true

846

false

# deberta-large-japanese-aozora ## Model Description This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune `deberta-large-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora-ud-head), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora") ``` ## Reference 安岡孝一: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43.

6f18c7da462d3b28dd66dcb87b2f5a3b

spacy/ca_core_news_md

spacy

null

28

5

spacy

0

token-classification

false

gpl-3.0

['ca']

null

0

['spacy', 'token-classification']

false

true

16,957

false

### Details: https://spacy.io/models/ca#ca_core_news_md Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `ca_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) | | **Sources** | [UD Catalan AnCora v2.8](https://github.com/UniversalDependencies/UD_Catalan-AnCora) (Martínez Alonso, Héctor; Pascual, Elena; Zeman, Daniel) [UD Catalan AnCora v2.8 + NER v3.2.8](https://github.com/TeMU-BSC/spacy/releases/tag/3.2.8) (Carlos Rodríguez-Penagos and Carme Armentano-Oller) [Catalan Lemmatizer](https://github.com/explosion/spacy-lookups-data) (Text Mining Unit, Barcelona Supercomputing Center) [Catalan Word Embeddings in FastText (Version 1.0)](http://doi.org/10.5281/zenodo.4522041) (Gutiérrez-Fandiño, Asier, Armengol-Estapé, Jordi, Gonzalez-Agirre, Aitor, Carrino, Casimiro Pio, de Gibert, Ona, & Villegas, Marta) | | **License** | `GNU GPL 3.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (317 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumForm=Digit\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Comm`, `POS=AUX\|VerbForm=Inf`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Peri`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `POS=SCONJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `POS=NOUN`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `POS=SYM`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|Polarity=Neg`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Ind`, `POS=PUNCT`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `POS=AUX`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=VERB`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `AdvType=Tim\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Semi`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Dash`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=SPACE`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Colo`, `Gender=Masc\|NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Quot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `POS=VERB`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PronType=Prs`, `POS=X`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `NumForm=Digit\|NumType=Ord\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=PRON\|PronType=Int`, `Foreign=Yes\|Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Foreign=Yes\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Comm`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Comm`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `AdvType=Tim\|Degree=Cmp\|POS=ADV`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Pre\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `POS=INTJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=SCONJ`, `Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=SYM`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=DET`, `Foreign=Yes\|POS=ADV`, `POS=PUNCT\|PunctSide=Fin\|Punta d'aignctType=Brck`, `Degree=Cmp\|POS=ADJ`, `AdvType=Tim\|POS=SYM`, `Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.93 | | `TOKEN_P` | 99.78 | | `TOKEN_R` | 99.79 | | `TOKEN_F` | 99.79 | | `POS_ACC` | 98.42 | | `MORPH_ACC` | 98.05 | | `MORPH_MICRO_P` | 99.45 | | `MORPH_MICRO_R` | 98.93 | | `MORPH_MICRO_F` | 99.19 | | `SENTS_P` | 99.18 | | `SENTS_R` | 99.18 | | `SENTS_F` | 99.18 | | `DEP_UAS` | 91.88 | | `DEP_LAS` | 88.92 | | `TAG_ACC` | 98.42 | | `LEMMA_ACC` | 98.02 | | `ENTS_P` | 84.34 | | `ENTS_R` | 83.63 | | `ENTS_F` | 83.98 |

5a2b86dc852a7fc79ebe80d5f2205063

Graphcore/gpt2-medium-wikitext-103

Graphcore

gpt2

15

3

transformers

1

text-generation

true

false

apache-2.0

null

['wikitext']

null

0

['generated_from_trainer']

true

3,854

false

# Graphcore/gpt2-medium-wikitext-103 Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. ## Model description GPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation. Paper link : [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) ## Intended uses & limitations This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the [wikitext-103-raw-v1](https://huggingface.co/datasets/wikitext) dataset. It achieves the following results on the evaluation set: - Loss: 2.6973 ## Training and evaluation data Trained on wikipedia dataset: - [HuggingFace/wikitext-103-raw-v1](https://huggingface.co/datasets/wikitext) dataset ## Training procedure Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). Command line: ``` python examples/language-modeling/run_clm.py \ --model_name_or_path gpt2-medium \ --ipu_config_name Graphcore/gpt2-medium-ipu \ --dataset_name wikitext \ --dataset_config_name wikitext-103-raw-v1 \ --do_train \ --do_eval \ --num_train_epochs 10 \ --dataloader_num_workers 64 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --gradient_accumulation_steps 256 \ --output_dir /tmp/clm_output_medium \ --logging_steps 5 \ --learning_rate 1e-5 \ --lr_scheduler_type linear \ --loss_scaling 16384 \ --weight_decay 0.01 \ --warmup_ratio 0.1 \ --ipu_config_overrides="embedding_serialization_factor=5,inference_device_iterations=9,replication_factor=2,inference_replication_factor=2,ipus_per_replica=8,layers_per_ipu=[0 3 3 3 3 4 4 4],matmul_proportion=0.25" \ --dataloader_drop_last \ --pod_type pod16 ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 256 - total_train_batch_size: 1024 - total_eval_batch_size: 18 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 - training precision: Mixed Precision ### Training results ``` ***** train metrics ***** "epoch": 10.0, "train_loss": 2.8070910754504506, "train_runtime": 11217.8167, "train_samples": 114248, "train_samples_per_second": 101.845, "train_steps_per_second": 0.099 ***** eval metrics ***** "eval_loss": 2.697265625, "eval_samples": 240, "perplexity": 14.83910053420958 ``` ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6

6598b8602322fb7b692557769e65ded0

Karnezis/finetuning-sentiment-model-3000-samples

Karnezis

distilbert

19

12

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,055

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3136 - Accuracy: 0.8767 - F1: 0.8771 ## 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: 2 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

be70f47e07a642c7f209b913c21858eb

Rocketknight1/distilgpt2-finetuned-wikitext2

Rocketknight1

gpt2

19

26

transformers

0

text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,192

false

# Rocketknight1/distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8577 - Validation Loss: 3.6752 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.8577 | 3.6752 | 0 | ### Framework versions - Transformers 4.16.0.dev0 - TensorFlow 2.8.0-rc0 - Datasets 1.17.0 - Tokenizers 0.11.0

fb69c73ae3cd38d3e1330d76c9dc553e

Luciano/xlm-roberta-base-finetuned-lener_br-finetuned-lener-br

Luciano

xlm-roberta

12

7

transformers

0

token-classification

true

false

mit

['pt']

['lener_br']

null

4

2

0

['generated_from_trainer']

true

2,767

false

# xlm-roberta-base-finetuned-lener_br-finetuned-lener-br This model is a fine-tuned version of [Luciano/xlm-roberta-base-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-base-finetuned-lener_br) on the lener_br dataset. It achieves the following results on the evaluation set: - Loss: nan - Precision: 0.9206 - Recall: 0.9294 - F1: 0.9250 - Accuracy: 0.9833 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0657 | 1.0 | 1957 | nan | 0.7780 | 0.8687 | 0.8209 | 0.9718 | | 0.0321 | 2.0 | 3914 | nan | 0.8755 | 0.8708 | 0.8731 | 0.9793 | | 0.0274 | 3.0 | 5871 | nan | 0.8096 | 0.9124 | 0.8579 | 0.9735 | | 0.0216 | 4.0 | 7828 | nan | 0.7913 | 0.8842 | 0.8352 | 0.9718 | | 0.0175 | 5.0 | 9785 | nan | 0.7735 | 0.9248 | 0.8424 | 0.9721 | | 0.0117 | 6.0 | 11742 | nan | 0.9206 | 0.9294 | 0.9250 | 0.9833 | | 0.0121 | 7.0 | 13699 | nan | 0.8988 | 0.9318 | 0.9150 | 0.9819 | | 0.0086 | 8.0 | 15656 | nan | 0.8922 | 0.9175 | 0.9047 | 0.9801 | | 0.007 | 9.0 | 17613 | nan | 0.8482 | 0.8997 | 0.8732 | 0.9769 | | 0.0051 | 10.0 | 19570 | nan | 0.8730 | 0.9274 | 0.8994 | 0.9798 | | 0.0045 | 11.0 | 21527 | nan | 0.9172 | 0.9051 | 0.9111 | 0.9819 | | 0.0014 | 12.0 | 23484 | nan | 0.9138 | 0.9155 | 0.9147 | 0.9823 | | 0.0029 | 13.0 | 25441 | nan | 0.9099 | 0.9287 | 0.9192 | 0.9834 | | 0.0035 | 14.0 | 27398 | nan | 0.9019 | 0.9294 | 0.9155 | 0.9831 | | 0.0005 | 15.0 | 29355 | nan | 0.8886 | 0.9343 | 0.9109 | 0.9825 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

8f511f38e280e28920057863e61a9251

okho0653/Bio_ClinicalBERT-zero-shot-finetuned-all-cad

okho0653

bert

13

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

972

false

# Bio_ClinicalBERT-zero-shot-finetuned-all-cad This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None 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: 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: 2 ### Training results ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

ddea40bb3f2c7f00ebd955378af2fca4

jonatasgrosman/exp_w2v2t_fa_unispeech-ml_s998

jonatasgrosman

unispeech

10

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fa']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'fa']

false

true

500

false

# exp_w2v2t_fa_unispeech-ml_s998 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (fa)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

23656522f30941488e778548280c9ecc

nandysoham16/14-clustered_aug

nandysoham16

distilbert

8

0

keras

0

null

false

true

false

mit

['en']

null

0

[]

false

true

4,805

false

# Model Card for Model ID  # Model Details ## Model Description  ['The_Legend_of_Zelda:_Twilight_Princess', 'Symbiosis', 'Tristan_da_Cunha', 'Hokkien', 'Thuringia', 'Samoa', 'Chinese_characters', 'Digimon', 'Tuvalu', 'Geological_history_of_Earth'] - **Developed by:** nandysoham - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** [More Information Needed] ## Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] # Uses  ## Direct Use  [More Information Needed] ## Downstream Use [optional]  [More Information Needed] ## Out-of-Scope Use  [More Information Needed] # Bias, Risks, and Limitations  [More Information Needed] ## Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] # Training Details ## Training Data  [More Information Needed] ## Training Procedure [optional]  ### Preprocessing [More Information Needed] ### Speeds, Sizes, Times  [More Information Needed] # Evaluation  ## Testing Data, Factors & Metrics ### Testing Data  [More Information Needed] ### Factors  [More Information Needed] ### Metrics  [More Information Needed] ## Results [More Information Needed] ### Summary # Model Examination [optional]  [More Information Needed] # 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 [optional] ## Model Architecture and Objective [More Information Needed] ## Compute Infrastructure [More Information Needed] ### Hardware [More Information Needed] ### Software [More Information Needed] # Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] # Glossary [optional]  [More Information Needed] # More Information [optional] [More Information Needed] # Model Card Authors [optional] [More Information Needed] # Model Card Contact [More Information Needed]

3b308a6668dc2d5a5c9e7fac8c68eb01

Graphcore/groupbert-base-uncased

Graphcore

groupbert

14

1,128

transformers

1

null

true

false

apache-2.0

['en']

['Graphcore/wikipedia-bert-128', 'Graphcore/wikipedia-bert-512']

null

1

0

1

0

['generated_from_trainer']

true

7,551

false

# Graphcore/groupbert-base-uncased Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. ## Model description GroupBERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed by Graphcore to pretrain bidirectional representations from unlabelled texts. GroupBERT uses grouped convolutions and matmuls in the encoder, which allows to parallelize computation and achieve higher parameter efficiency. More details are described in the [GroupBERT paper](https://arxiv.org/pdf/2106.05822.pdf). It was trained with two objectives in pretraining : Masked language modelling (MLM) and Next sentence prediction(NSP). First, MLM is different from traditional LM which sees the words one after another while BERT allows the model to learn a bidirectional representation. In addition to MLM, NSP is used for jointly pertaining text-pair representations. Similarly to BERT it enables easy and fast fine-tuning for different downstream tasks such as Sequence Classification, Named Entity Recognition, Question Answering, Multiple Choice and MaskedLM. It reduces the need of many engineering efforts for building task specific architectures through pre-trained representation. And achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks. ## Intended uses & limitations This model is a pre-trained GroupBERT-Base trained in two phases on the [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) and [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) datasets. It was trained on a Graphcore IPU-POD16 using [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore). Graphcore and Hugging Face are working together to make training of Transformer models on IPUs fast and easy. Learn more about how to take advantage of the power of Graphcore IPUs to train Transformers models at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). ## Training and evaluation data Trained on wikipedia datasets: - [Graphcore/wikipedia-bert-128](https://huggingface.co/datasets/Graphcore/wikipedia-bert-128) - [Graphcore/wikipedia-bert-512](https://huggingface.co/datasets/Graphcore/wikipedia-bert-512) ## Fine-tuning with these weights These weights can be used in either `transformers` or [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore). For example, to fine-tune the SQuAD v1 with `optimum-graphcore` you can do: ``` python examples/question-answering/run_qa.py \ --model_name_or_path Graphcore/groupbert-base-uncased \ --ipu_config_name Graphcore/groupbert-base-uncased \ --dataset_name squad \ --version_2_with_negative False \ --do_train \ --do_eval \ --pad_on_batch_axis \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 16 \ --gradient_accumulation_steps 10 \ --pod_type pod16 \ --learning_rate 4e-4 \ --max_seq_length 384 \ --doc_stride 128 \ --seed 42 \ --lr_scheduler_type linear \ --lamb \ --loss_scaling 64 \ --weight_decay 0.01 \ --warmup_ratio 0.1 \ --logging_steps 5 \ --save_steps -1 \ --dataloader_num_workers 64 \ --output_dir output/squad_groupbert_base ``` ## Training procedure Trained MLM and NSP pre-training scheme from [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://arxiv.org/abs/1904.00962). Trained on a Graphcore IPU-POD16 using [`optimum-graphcore`](https://github.com/huggingface/optimum-graphcore). It was trained with the IPUConfig [Graphcore/bert-base-ipu](https://huggingface.co/Graphcore/bert-base-ipu/). Command lines: Phase 1: ``` python examples/language-modeling/run_pretraining.py \ --model_type groupbert \ --tokenizer_name bert-base-uncased \ --ipu_config_name Graphcore/bert-base-ipu \ --dataset_name Graphcore/wikipedia-bert-128 \ --do_train \ --logging_steps 5 \ --max_seq_length 128 \ --max_steps 10500 \ --is_already_preprocessed \ --dataloader_num_workers 64 \ --dataloader_mode async_rebatched \ --lamb \ --per_device_train_batch_size 8 \ --gradient_accumulation_steps 2000 \ --pod_type pod16 \ --learning_rate 0.012 \ --loss_scaling 16384 \ --weight_decay 0.01 \ --warmup_ratio 0.15 \ --groupbert_schedule \ --config_overrides "hidden_dropout_prob=0.0,attention_probs_dropout_prob=0.0" \ --ipu_config_overrides device_iterations="1,matmul_proportion=0.22,layers_per_ipu=[1 3 4 4]" \ --output_dir output-pretrain-groupbert-base-phase1 ``` Phase 2: ``` python examples/language-modeling/run_pretraining.py \ --model_type groupbert \ --tokenizer_name bert-base-uncased \ --ipu_config_name Graphcore/bert-base-ipu \ --dataset_name Graphcore/wikipedia-bert-512 \ --model_name_or_path ./output-pretrain-bert-base-phase1 \ --do_train \ --logging_steps 5 \ --max_seq_length 512 \ --max_steps 2038 \ --is_already_preprocessed \ --dataloader_num_workers 128 \ --dataloader_mode async_rebatched \ --lamb \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 2048 \ --pod_type pod16 \ --learning_rate 0.01 \ --loss_scaling 128 \ --weight_decay 0.01 \ --warmup_ratio 0.15 \ --groupbert_schedule \ --config_overrides "hidden_dropout_prob=0.0,attention_probs_dropout_prob=0.0" \ --ipu_config_overrides "device_iterations=1,embedding_serialization_factor=2,matmul_proportion=0.22,layers_per_ipu=[1 3 4 4]" \ --output_dir output-pretrain-groupbert-base-phase2 ``` ### Training hyperparameters The following hyperparameters were used during phase 1 training: - learning_rate: 0.012 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 200 - total_train_batch_size: 64000 - total_eval_batch_size: 20 - optimizer: LAMB - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - training_steps: 10500 - training precision: Mixed Precision The following hyperparameters were used during phase 2 training: - learning_rate: 0.01 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 2048 - total_train_batch_size: 16384 - total_eval_batch_size: 20 - optimizer: LAMB - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - training_steps: 2038 - training precision: Mixed Precision ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.10.0+cpu - Datasets 2.6.1 - Tokenizers 0.12.1

44a49877783e91806822b76229070271

ViktorDo/distilbert-base-uncased-finetuned-powo_all

ViktorDo

distilbert

4

2

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,365

false

# distilbert-base-uncased-finetuned-powo_all This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -343, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1

36fd5ed06a9df53d6c04ddd5b2c75984

ShadoWxShinigamI/SD-2-MJart

ShadoWxShinigamI

null

4

0

null

17

null

false

creativeml-openrail-m

null

0

1

0

[]

false

true

957

false

##Textual Inversion Embed + Hypernetwork For SD 2 models by ShadoWxShinigamI Trained on 200 BLIP Captioned images from my personal MJ Generations. Meant to be used with 768 Models. 16 Vectors - 625 Steps - TI Embed Swish - 10000 Steps - Hypernetwork. The Hypernetwork is meant to be an augment to be used alongside the embed. Using at 0.5 Strength tends to produce the best output (YMMV) Examples :- ![image.png](https://s3.amazonaws.com/moonup/production/uploads/1670827476778-633a520aecbd8b19357b4806.png) ![00001-335098425.png](https://s3.amazonaws.com/moonup/production/uploads/1670828191063-633a520aecbd8b19357b4806.png) ![anime.png](https://s3.amazonaws.com/moonup/production/uploads/1670828241828-633a520aecbd8b19357b4806.png) ![monkey.png](https://s3.amazonaws.com/moonup/production/uploads/1670828303588-633a520aecbd8b19357b4806.png) ![panda.png](https://s3.amazonaws.com/moonup/production/uploads/1670828302002-633a520aecbd8b19357b4806.png)

62b12d2b1250275c1e024131a97592f9

HAriGa/my_awesome_model

HAriGa

bert

14

17

transformers

0

text-classification

true

false

mit

null

['gnad10']

null

0

['generated_from_trainer']

true

1,264

false

# my_awesome_model This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the gnad10 dataset. It achieves the following results on the evaluation set: - Loss: 0.3414 - F1: 0.9001 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5884 | 1.0 | 578 | 0.3510 | 0.8940 | | 0.2389 | 2.0 | 1156 | 0.3414 | 0.9001 | ### Framework versions - Transformers 4.26.0 - Pytorch 2.0.0.dev20230126+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

a587b6b812d092941d05675e98a7b228

pritoms/distilgpt2-YTTranscriptTrial2

pritoms

gpt2

9

4

transformers

0

text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,243

false

# distilgpt2-YTTranscriptTrial2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.8738 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 70 | 6.0027 | | No log | 2.0 | 140 | 5.9072 | | No log | 3.0 | 210 | 5.8738 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

141816358dc304eef378c7613e4117b1

anuragshas/whisper-large-v2-as

anuragshas

whisper

22

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['as']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,389

false

# Whisper Large-v2 Assamese This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 as dataset. It achieves the following results on the evaluation set: - Loss: 0.3451 - Wer: 23.6961 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0008 | 8.47 | 500 | 0.3451 | 23.6961 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

bc4956fa8358f5b41715b04c297bc6f4

adityay1221/cat.5.32

adityay1221

t5

9

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

979

false

# cat.5.32 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0293 - Bleu: 25.3811 ## 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 121 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1

473f169b463d5a5f71335f5ec81c4c74

tkesonia/xlm-roberta-base-finetuned-marc-en

tkesonia

xlm-roberta

14

3

transformers

0

text-classification

true

false

mit

null

['amazon_reviews_multi']

null

0

['generated_from_trainer']

true

1,274

false

# xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9211 - Mae: 0.5122 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1436 | 1.0 | 235 | 1.0181 | 0.5366 | | 0.9756 | 2.0 | 470 | 0.9211 | 0.5122 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

05415165d2b55de5ce9cdc574b2a4415

timm/convnext_nano.in12k_ft_in1k

timm

null

4

3,490

timm

0

image-classification

true

false

apache-2.0

null

['imagenet-1k', 'imagenet-12k']

null

0

['image-classification', 'timm']

false

true

21,681

false

# Model card for convnext_nano.in12k_ft_in1k A ConvNeXt image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. Fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.6 - GMACs: 2.5 - Activations (M): 8.4 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/rwightman/pytorch-image-models - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_nano.in12k_ft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_nano.in12k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g. for convnext_base: # torch.Size([1, 128, 56, 56]) # torch.Size([1, 256, 28, 28]) # torch.Size([1, 512, 14, 14]) # torch.Size([1, 1024, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_nano.in12k_ft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled (ie.e a (batch_size, num_features, H, W) tensor output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor ``` ## Model Comparison ### By Top-1 All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. |model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| |[convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512)|88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 | |[convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384)|88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 | |[convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384)|88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 | |[convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384)|87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 | |[convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384)|87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 | |[convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384)|87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 | |[convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k)|87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 | |[convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k)|87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 | |[convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k)|87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 | |[convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384)|86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 | |[convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k)|86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 | |[convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k)|86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 | |[convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384)|86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 | |[convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k)|86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 | |[convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384)|86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 | |[convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k)|86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 | |[convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k)|85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 | |[convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384)|85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 | |[convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k)|85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 | |[convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k)|85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 | |[convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384)|85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384)|85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 | |[convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k)|84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 | |[convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k)|84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 | |[convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k)|84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 | |[convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k)|84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 | |[convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384)|84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k)|83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 | |[convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k)|83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384)|83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 | |[convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k)|83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 | |[convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k)|82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 | |[convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)|82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 | |[convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k)|82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 | |[convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k)|82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 | |[convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k)|82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k)|82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 | |[convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k)|81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 | |[convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k)|80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 | |[convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k)|80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 | |[convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k)|80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 | |[convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k)|79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 | |[convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k)|79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 | |[convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k)|78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 | |[convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k)|77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 | |[convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k)|77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 | |[convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k)|76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 | |[convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k)|75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 | |[convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k)|75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 | ### By Throughput (samples / sec) All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. |model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size| |----------------------------------------------|------|------|--------|-----------|------|------|---------------|----------| |[convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k)|75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 | |[convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k)|75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 | |[convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k)|77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 | |[convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k)|77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 | |[convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k)|79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 | |[convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k)|79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 | |[convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k)|76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 | |[convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k)|78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 | |[convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k)|82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 | |[convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k)|80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 | |[convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k)|80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 | |[convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k)|80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 | |[convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k)|82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 | |[convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k)|82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 | |[convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k)|84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 | |[convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k)|82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 | |[convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k)|81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k)|82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 | |[convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k)|84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 | |[convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k)|85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 | |[convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k)|83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k)|83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 | |[convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k)|82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 | |[convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k)|83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 | |[convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k)|85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 | |[convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384)|84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 | |[convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384)|85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 | |[convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k)|86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 | |[convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384)|83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 | |[convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k)|84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 | |[convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k)|86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 | |[convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k)|84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 | |[convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k)|86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 | |[convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384)|85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 | |[convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384)|86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 | |[convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384)|85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 | |[convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k)|87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 | |[convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k)|87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 | |[convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k)|85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 | |[convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384)|86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 | |[convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k)|87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 | |[convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384)|86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 | |[convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384)|87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 | |[convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384)|87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 | |[convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k)|86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 | |[convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384)|88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 | |[convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384)|87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 | |[convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384)|88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 | |[convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512)|88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 | ## Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` ```bibtex @article{liu2022convnet, author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ```

21c1b955afc8150bdf1881d16c2703c4

tzvc/375132eb-61b1-49ec-83f3-04676640d6c9

tzvc

null

31

2

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image']

false

true

1,743

false

### 375132eb-61b1-49ec-83f3-04676640d6c9 Dreambooth model trained by tzvc 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: sdcid (use that on your prompt) ![sdcid 0](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%281%29.jpg)![sdcid 1](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%282%29.jpg)![sdcid 2](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%283%29.jpg)![sdcid 3](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%284%29.jpg)![sdcid 4](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%285%29.jpg)![sdcid 5](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%286%29.jpg)![sdcid 6](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%287%29.jpg)![sdcid 7](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%288%29.jpg)![sdcid 8](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%289%29.jpg)![sdcid 9](https://huggingface.co/tzvc/375132eb-61b1-49ec-83f3-04676640d6c9/resolve/main/concept_images/sdcid_%2810%29.jpg)

07f18fbf18cbe1ed96b1a69e9e21f1d2

aXhyra/demo_hate_31415

aXhyra

distilbert

10

6

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,387

false

# demo_hate_31415 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.8697 - F1: 0.7773 ## 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: 7.320702985778492e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 282 | 0.4850 | 0.7645 | | 0.3877 | 2.0 | 564 | 0.5160 | 0.7856 | | 0.3877 | 3.0 | 846 | 0.6927 | 0.7802 | | 0.1343 | 4.0 | 1128 | 0.8697 | 0.7773 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

805efd4f36985070c25e2727c3cd19c9

pig4431/TUF_ELECTRA_5E

pig4431

electra

10

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

4,254

false

# TUF_ELECTRA_5E This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1195 - Accuracy: 0.94 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6021 | 0.1 | 50 | 0.5770 | 0.7533 | | 0.5301 | 0.2 | 100 | 0.5460 | 0.7533 | | 0.4958 | 0.3 | 150 | 0.4943 | 0.7533 | | 0.4347 | 0.4 | 200 | 0.4112 | 0.8467 | | 0.3565 | 0.5 | 250 | 0.3601 | 0.88 | | 0.3515 | 0.59 | 300 | 0.3465 | 0.9 | | 0.301 | 0.69 | 350 | 0.3214 | 0.9067 | | 0.2963 | 0.79 | 400 | 0.2996 | 0.9 | | 0.2848 | 0.89 | 450 | 0.2511 | 0.9267 | | 0.2548 | 0.99 | 500 | 0.2493 | 0.8933 | | 0.2527 | 1.09 | 550 | 0.2381 | 0.9333 | | 0.2484 | 1.19 | 600 | 0.2099 | 0.9333 | | 0.2267 | 1.29 | 650 | 0.1834 | 0.9333 | | 0.2147 | 1.39 | 700 | 0.1919 | 0.94 | | 0.1961 | 1.49 | 750 | 0.1751 | 0.9333 | | 0.1868 | 1.58 | 800 | 0.1772 | 0.9267 | | 0.2393 | 1.68 | 850 | 0.1726 | 0.92 | | 0.1747 | 1.78 | 900 | 0.1509 | 0.9467 | | 0.2236 | 1.88 | 950 | 0.1532 | 0.94 | | 0.174 | 1.98 | 1000 | 0.1752 | 0.9267 | | 0.1983 | 2.08 | 1050 | 0.1563 | 0.94 | | 0.2015 | 2.18 | 1100 | 0.1494 | 0.9467 | | 0.1563 | 2.28 | 1150 | 0.1876 | 0.9333 | | 0.168 | 2.38 | 1200 | 0.1802 | 0.9333 | | 0.2074 | 2.48 | 1250 | 0.1669 | 0.94 | | 0.1726 | 2.57 | 1300 | 0.1348 | 0.9533 | | 0.1373 | 2.67 | 1350 | 0.1549 | 0.9533 | | 0.1694 | 2.77 | 1400 | 0.1339 | 0.96 | | 0.1782 | 2.87 | 1450 | 0.1417 | 0.9533 | | 0.1771 | 2.97 | 1500 | 0.1228 | 0.96 | | 0.1886 | 3.07 | 1550 | 0.1415 | 0.9533 | | 0.1507 | 3.17 | 1600 | 0.1350 | 0.9467 | | 0.1435 | 3.27 | 1650 | 0.1294 | 0.9467 | | 0.1548 | 3.37 | 1700 | 0.1316 | 0.96 | | 0.1475 | 3.47 | 1750 | 0.1314 | 0.9333 | | 0.1764 | 3.56 | 1800 | 0.1195 | 0.94 | | 0.1668 | 3.66 | 1850 | 0.1199 | 0.94 | | 0.1336 | 3.76 | 1900 | 0.1210 | 0.9467 | | 0.1452 | 3.86 | 1950 | 0.1259 | 0.9467 | | 0.206 | 3.96 | 2000 | 0.1247 | 0.96 | | 0.1704 | 4.06 | 2050 | 0.1253 | 0.9533 | | 0.1489 | 4.16 | 2100 | 0.1194 | 0.94 | | 0.1766 | 4.26 | 2150 | 0.1278 | 0.96 | | 0.1387 | 4.36 | 2200 | 0.1179 | 0.94 | | 0.1269 | 4.46 | 2250 | 0.1270 | 0.96 | | 0.154 | 4.55 | 2300 | 0.1208 | 0.94 | | 0.1481 | 4.65 | 2350 | 0.1210 | 0.94 | | 0.1676 | 4.75 | 2400 | 0.1196 | 0.94 | | 0.1202 | 4.85 | 2450 | 0.1194 | 0.94 | | 0.1323 | 4.95 | 2500 | 0.1195 | 0.94 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

c8077571b48a42aabbe8e99f8f0eb3d5

PlanTL-GOB-ES/ca_anonimization_core_lg

PlanTL-GOB-ES

null

23

5

spacy

0

token-classification

false

mit

['es', 'ca']

null

0

['spacy', 'token-classification']

false

true

16,424

false

This is a Spacy multilingual (Catalan & Spanish) anonimization model, for use with BSC's AnonymizationPipeline at: https://github.com/TeMU-BSC/AnonymizationPipeline. The anonymization pipeline is a library for performing sensitive data identification and ultimately anonymization of the detected data in Spanish and Catalan user generated plain text. This is not a standalone model and is meant to work within the pipeline. The model can detect the following entities: `EMAIL`, `FINANCIAL`, `ID`, `LOC`, `MISC`, `ORG`, `PER`, `TELEPHONE`, `VEHICLE`, `ZIP` | Feature | Description | | --- | --- | | **Name** | `ca_anonimization_core_lg` | | **Version** | `1.0.0` | | **spaCy** | `>=3.2.3,<3.3.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | `MIT` | | **Author** | [Joaquin Silveira](https://github.com/TeMU-BSC/AnonymizationPipeline) | ### Label Scheme <details> <summary>View label scheme (322 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumForm=Digit\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Comm`, `POS=AUX\|VerbForm=Inf`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Peri`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `POS=SCONJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `POS=NOUN`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `POS=SYM`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|Polarity=Neg`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Ind`, `POS=PUNCT`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `POS=AUX`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=VERB`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `AdvType=Tim\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Semi`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Dash`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Colo`, `Gender=Masc\|NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Quot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `POS=VERB`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PronType=Prs`, `POS=X`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `NumForm=Digit\|NumType=Ord\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=PRON\|PronType=Int`, `Foreign=Yes\|Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Foreign=Yes\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Comm`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Comm`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `AdvType=Tim\|Degree=Cmp\|POS=ADV`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Pre\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `POS=INTJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=SCONJ`, `Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=SYM`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=DET`, `Foreign=Yes\|POS=ADV`, `POS=PUNCT\|PunctSide=Fin\|Punta d'aignctType=Brck`, `Degree=Cmp\|POS=ADJ`, `AdvType=Tim\|POS=SYM`, `Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `EMAIL`, `FINANCIAL`, `ID`, `LOC`, `MISC`, `ORG`, `PER`, `TELEPHONE`, `VEHICLE`, `ZIP` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 69.12 | | `ENTS_P` | 74.60 | | `ENTS_R` | 64.38 | | `NER_LOSS` | 26573.78 |

dd7cdfef753a925cba62fc9f10e7b9f9

henryscheible/mnli_roberta-base_125

henryscheible

null

14

0

null

0

null

true

false

mit

['en']

['glue']

null

0

['generated_from_trainer']

true

1,003

false

# mnli_roberta-base_125 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3804 - Accuracy: 0.8695 ## 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: 400 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

11677a9c11c9ba229ec51b59bde161cf

AkshatSurolia/BEiT-FaceMask-Finetuned

AkshatSurolia

beit

10

6

transformers

0

image-classification

true

false

apache-2.0

null

['Face-Mask18K']

null

0

['image-classification']

false

true

2,495

false

# BEiT for Face Mask Detection BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. ## Model description The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. The pre-training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL-E's VQ-VAE, based on masked patches. Next, the model was fine-tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the [CLS] token. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. ## Training Metrics epoch = 0.55 total_flos = 576468516GF train_loss = 0.151 train_runtime = 0:58:16.56 train_samples_per_second = 16.505 train_steps_per_second = 1.032 --- ## Evaluation Metrics epoch = 0.55 eval_accuracy = 0.975 eval_loss = 0.0803 eval_runtime = 0:03:13.02 eval_samples_per_second = 18.629 eval_steps_per_second = 2.331

f5843d670fad8c0545f9ac510084f965

jonatasgrosman/exp_w2v2t_es_wav2vec2_s596

jonatasgrosman

wav2vec2

10

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'es']

false

true

456

false

# exp_w2v2t_es_wav2vec2_s596 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

7f5595e31ce52cd8005a185d4e5d0285

tau/splinter-base

tau

splinter

7

971

transformers

1

question-answering

true

false

apache-2.0

['en']

null

0

['splinter', 'SplinterModel']

false

true

2,593

false

# Splinter base model Splinter-base is the pretrained model discussed in the paper [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) (at ACL 2021). Its original repository can be found [here](https://github.com/oriram/splinter). The model is case-sensitive. Note: This model **doesn't** contain the pretrained weights for the QASS layer (see paper for details), and therefore the QASS layer is randomly initialized upon loading it. For the model **with** those weights, see [tau/splinter-base-qass](https://huggingface.co/tau/splinter-base-qass). ## Model description Splinter is a model that is pretrained in a self-supervised fashion for few-shot question answering. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Recurring Span Selection (RSS) objective, which emulates the span selection process involved in extractive question answering. Given a text, clusters of recurring spans (n-grams that appear more than once in the text) are first identified. For each such cluster, all of its instances but one are replaced with a special `[QUESTION]` token, and the model should select the correct (i.e., unmasked) span for each masked one. The model also defines the Question-Aware Span selection (QASS) layer, which selects spans conditioned on a specific question (in order to perform multiple predictions). ## Intended uses & limitations The prime use for this model is few-shot extractive QA. ## Pretraining The model was pretrained on a v3-8 TPU for 2.4M steps. The training data is based on **Wikipedia** and **BookCorpus**. See the paper for more details. ### BibTeX entry and citation info ```bibtex @inproceedings{ram-etal-2021-shot, title = "Few-Shot Question Answering by Pretraining Span Selection", author = "Ram, Ori and Kirstain, Yuval and Berant, Jonathan and Globerson, Amir and Levy, Omer", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.239", doi = "10.18653/v1/2021.acl-long.239", pages = "3066--3079", } ```

f186f5d83e196f2fa807b46a59596fc3

MeshalAlamr/wav2vec2-xls-r-300m-arabic_speech_commands_10s_one_speaker_all_classes_3_aug

MeshalAlamr

wav2vec2

10

3

transformers

0

audio-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

5,025

false

# wav2vec2-xls-r-300m-arabic_speech_commands_10s_one_speaker_all_classes_3_aug This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1190 - Accuracy: 0.7137 ## 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: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.6888 | 0.96 | 12 | 3.6887 | 0.025 | | 3.8686 | 1.96 | 24 | 3.6837 | 0.0488 | | 3.844 | 2.96 | 36 | 3.5466 | 0.1062 | | 3.7114 | 3.96 | 48 | 3.2589 | 0.1133 | | 2.8339 | 4.96 | 60 | 2.9553 | 0.1883 | | 2.5667 | 5.96 | 72 | 2.8784 | 0.1963 | | 2.1911 | 6.96 | 84 | 2.6379 | 0.2771 | | 1.8461 | 7.96 | 96 | 2.8874 | 0.2929 | | 1.6044 | 8.96 | 108 | 2.4989 | 0.34 | | 1.0916 | 9.96 | 120 | 2.3111 | 0.425 | | 0.9371 | 10.96 | 132 | 2.0899 | 0.4829 | | 0.8177 | 11.96 | 144 | 2.0116 | 0.4971 | | 0.6366 | 12.96 | 156 | 2.0598 | 0.5558 | | 0.549 | 13.96 | 168 | 2.0084 | 0.5575 | | 0.2917 | 14.96 | 180 | 1.8231 | 0.6038 | | 0.2283 | 15.96 | 192 | 1.9943 | 0.6079 | | 0.2382 | 16.96 | 204 | 2.2098 | 0.6083 | | 0.2475 | 17.96 | 216 | 2.3519 | 0.5992 | | 0.1612 | 18.96 | 228 | 2.2483 | 0.5929 | | 0.133 | 19.96 | 240 | 2.2263 | 0.6079 | | 0.1301 | 20.96 | 252 | 2.6094 | 0.5683 | | 0.0993 | 21.96 | 264 | 2.0289 | 0.6417 | | 0.0779 | 22.96 | 276 | 1.9693 | 0.6479 | | 0.0824 | 23.96 | 288 | 2.2471 | 0.6258 | | 0.0872 | 24.96 | 300 | 2.3715 | 0.6538 | | 0.0694 | 25.96 | 312 | 2.5367 | 0.6325 | | 0.0704 | 26.96 | 324 | 2.4467 | 0.6388 | | 0.061 | 27.96 | 336 | 2.1581 | 0.6621 | | 0.0835 | 28.96 | 348 | 2.1672 | 0.6792 | | 0.0402 | 29.96 | 360 | 2.2166 | 0.6596 | | 0.0329 | 30.96 | 372 | 2.6316 | 0.6217 | | 0.0516 | 31.96 | 384 | 2.0840 | 0.6908 | | 0.0455 | 32.96 | 396 | 2.2299 | 0.67 | | 0.0449 | 33.96 | 408 | 2.4341 | 0.6733 | | 0.0332 | 34.96 | 420 | 2.2830 | 0.6725 | | 0.0334 | 35.96 | 432 | 2.2060 | 0.6829 | | 0.025 | 36.96 | 444 | 2.2836 | 0.6554 | | 0.0351 | 37.96 | 456 | 2.5417 | 0.6517 | | 0.0372 | 38.96 | 468 | 2.2738 | 0.6779 | | 0.0136 | 39.96 | 480 | 2.4606 | 0.6525 | | 0.0178 | 40.96 | 492 | 2.1996 | 0.675 | | 0.0116 | 41.96 | 504 | 2.2557 | 0.6763 | | 0.0113 | 42.96 | 516 | 2.2061 | 0.6863 | | 0.014 | 43.96 | 528 | 2.1279 | 0.6925 | | 0.015 | 44.96 | 540 | 2.2151 | 0.6871 | | 0.0197 | 45.96 | 552 | 2.1506 | 0.6929 | | 0.0102 | 46.96 | 564 | 2.1609 | 0.685 | | 0.0115 | 47.96 | 576 | 2.1685 | 0.6854 | | 0.0097 | 48.96 | 588 | 2.2892 | 0.6821 | | 0.0148 | 49.96 | 600 | 2.4085 | 0.6921 | | 0.0114 | 50.96 | 612 | 2.2171 | 0.7104 | | 0.0141 | 51.96 | 624 | 2.1458 | 0.7075 | | 0.0066 | 52.96 | 636 | 2.2046 | 0.7013 | | 0.0128 | 53.96 | 648 | 2.1424 | 0.705 | | 0.0063 | 54.96 | 660 | 2.1425 | 0.7075 | | 0.0094 | 55.96 | 672 | 2.1554 | 0.7087 | | 0.0161 | 56.96 | 684 | 2.1892 | 0.7063 | | 0.0067 | 57.96 | 696 | 2.1819 | 0.7067 | | 0.0099 | 58.96 | 708 | 2.1341 | 0.7125 | | 0.0067 | 59.96 | 720 | 2.1190 | 0.7137 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

cc1cafec8f2644fcebe1e766525d54cc

maretamasaeva/thesis-freeform-yesno

maretamasaeva

roberta

13

4

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,413

false

# thesis-freeform-yesno This model is a fine-tuned version of [maretamasaeva/thesis-freeform](https://huggingface.co/maretamasaeva/thesis-freeform) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4547 - Accuracy: 0.0194 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.5001 | 1.0 | 9052 | 2.4600 | 0.0194 | | 2.4921 | 2.0 | 18104 | 2.4595 | 0.0194 | | 2.4879 | 3.0 | 27156 | 2.4576 | 0.0194 | | 2.4793 | 4.0 | 36208 | 2.4547 | 0.0194 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1

bcfe60aca20098e46bd3a6bcbb2c5a44

pomercier/Francois_Legault

pomercier

null

24

20

diffusers

0

text-to-image

false

lgpl-3.0

null

2

0

2

0

['text-to-image']

false

true

1,270

false

### Francois Legault This is a Hugging Face model that utilizes Stable Diffusion 1.5, which is a technique used to improve the quality and stability of generated images. The model is prompted with the name "Francois Legault", the current Premier of Quebec, Canada, as input and generates an image of him, that could be a portrait, a photo of him in a meeting, him giving a speech, etc. The generated image can be used in a variety of applications, such as in creating avatars for virtual assistants, generating images for news articles, or creating personalized images for social media. For example, in a virtual assistant, the model can generate an image of Francois Legault, which can be used as an avatar for the virtual assistant. In a news article, the model can generate an image of Francois Legault giving a speech, which can be used as the featured image for the article. And in a social media, the model can generate an image of Francois Legault, which can be used as a personalized image for a user's profile. This model is a powerful tool for anyone looking to generate high-quality images based on a specific prompt. It can be used in a wide range of applications and can help save time and resources when it comes to creating images for various projects.

367eb63845186d5581e016416345bdf4

DOOGLAK/Tagged_Uni_100v9_NER_Model_3Epochs_AUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['tagged_uni100v9_wikigold_split']

null

0

['generated_from_trainer']

true

1,565

false

# Tagged_Uni_100v9_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni100v9_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4080 - Precision: 0.3227 - Recall: 0.2305 - F1: 0.2689 - Accuracy: 0.8557 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 39 | 0.4881 | 0.2185 | 0.0487 | 0.0797 | 0.8066 | | No log | 2.0 | 78 | 0.4431 | 0.2831 | 0.1536 | 0.1992 | 0.8387 | | No log | 3.0 | 117 | 0.4080 | 0.3227 | 0.2305 | 0.2689 | 0.8557 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

8edc917be658ede291538f7e4b2989d9

armamoyl/xlm-roberta-base-finetuned-panx-de

armamoyl

xlm-roberta

44

7

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,299

false

# 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: 0.1711 - F1: 0.0 ## 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: 6 - eval_batch_size: 6 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---:| | No log | 1.0 | 2097 | 0.1970 | 0.0 | | No log | 2.0 | 4194 | 0.1686 | 0.0 | | No log | 3.0 | 6291 | 0.1711 | 0.0 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu116 - Datasets 2.6.1 - Tokenizers 0.12.1

aefcd30186b4da89a9910ed39578db00

Wiebke/bert-base-casedepoch3_sexist_baseline_with_reddit_and_gab

Wiebke

bert

12

18

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,683

false

# bert-base-casedepoch3_sexist_baseline_with_reddit_and_gab This model is a fine-tuned version of [Wiebke/bert-base-casedepoch3_sexist_baseline](https://huggingface.co/Wiebke/bert-base-casedepoch3_sexist_baseline) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4434 - Accuracy: 0.8707 - F1: 0.8699 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0279 | 0.16 | 500 | 0.5257 | 0.8564 | 0.8540 | | 0.0273 | 0.31 | 1000 | 0.4614 | 0.8607 | 0.8607 | | 0.0235 | 0.47 | 1500 | 0.4873 | 0.8657 | 0.8620 | | 0.0201 | 0.63 | 2000 | 0.4544 | 0.8729 | 0.8694 | | 0.0215 | 0.78 | 2500 | 0.4597 | 0.865 | 0.8653 | | 0.0184 | 0.94 | 3000 | 0.4434 | 0.8707 | 0.8699 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

b1bcfd81b722f84bd82a964a37552903

ConvLab/roberta-base-trippy-dst-multiwoz21

ConvLab

roberta

4

12

transformers

0

null

true

false

apache-2.0

['en']

null

0

['dialogue state tracking', 'task-oriented dialog']

false

true

1,748

false

# roberta-base-trippy-dst-multiwoz21 This is a TripPy model trained on [MultiWOZ 2.1](https://github.com/budzianowski/multiwoz) for use in [ConvLab-3](https://github.com/ConvLab/ConvLab-3). This model predicts informable slots, requestable slots, general actions and domain indicator slots. Expected joint goal accuracy for MultiWOZ 2.1 is in the range of 55-56\%. For information about TripPy DST, refer to [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://aclanthology.org/2020.sigdial-1.4/). The training and evaluation code is available at the official [TripPy repository](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public). ## Training procedure The model was trained on MultiWOZ 2.1 data via supervised learning using the [TripPy codebase](https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public). MultiWOZ 2.1 data was loaded via ConvLab-3's unified data format dataloader. The pre-trained encoder is [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) (base). Fine-tuning the encoder and training the DST specific classification heads was conducted for 10 epochs. ### Training hyperparameters ``` python3 run_dst.py \ --task_name="unified" \ --model_type="roberta" \ --model_name_or_path="roberta-base" \ --dataset_config=dataset_config/unified_multiwoz21.json \ --do_lower_case \ --learning_rate=1e-4 \ --num_train_epochs=10 \ --max_seq_length=180 \ --per_gpu_train_batch_size=24 \ --per_gpu_eval_batch_size=32 \ --output_dir=results \ --save_epochs=2 \ --eval_all_checkpoints \ --warmup_proportion=0.1 \ --adam_epsilon=1e-6 \ --weight_decay=0.01 \ --fp16 \ --do_train \ --predict_type=dummy \ --seed=42 ```

f5d80b9ccb528893479603f7aeaeda7e

bookbot/wav2vec2-ljspeech-gruut

bookbot

wav2vec2

20

2,944

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['w11wo/ljspeech_phonemes']

null

0

['phoneme-recognition', 'generated_from_trainer']

true

6,730

false

# Wav2Vec2 LJSpeech Gruut Wav2Vec2 LJSpeech Gruut is an automatic speech recognition model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a fine-tuned version of [Wav2Vec2-Base](https://huggingface.co/facebook/wav2vec2-base) on the [LJSpech Phonemes](https://huggingface.co/datasets/w11wo/ljspeech_phonemes) dataset. Instead of being trained to predict sequences of words, this model was trained to predict sequence of phonemes, e.g. `["h", "ɛ", "l", "ˈoʊ", "w", "ˈɚ", "l", "d"]`. Therefore, the model's [vocabulary](https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/blob/main/vocab.json) contains the different IPA phonemes found in [gruut](https://github.com/rhasspy/gruut). This model was trained using HuggingFace's PyTorch framework. All training was done on a Google Cloud Engine VM with a Tesla A100 GPU. All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/tree/main) tab, as well as the [Training metrics](https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/tensorboard) logged via Tensorboard. ## Model | Model | #params | Arch. | Training/Validation data (text) | | ------------------------- | ------- | ----------- | ------------------------------- | | `wav2vec2-ljspeech-gruut` | 94M | wav2vec 2.0 | `LJSpech Phonemes` Dataset | ## Evaluation Results The model achieves the following results on evaluation: | Dataset | PER (w/o stress) | CER (w/o stress) | | ---------------------------- | :--------------: | :--------------: | | `LJSpech Phonemes` Test Data | 0.99% | 0.58% | ## Usage ```py from transformers import AutoProcessor, AutoModelForCTC, Wav2Vec2Processor import librosa import torch from itertools import groupby from datasets import load_dataset def decode_phonemes( ids: torch.Tensor, processor: Wav2Vec2Processor, ignore_stress: bool = False ) -> str: """CTC-like decoding. First removes consecutive duplicates, then removes special tokens.""" # removes consecutive duplicates ids = [id_ for id_, _ in groupby(ids)] special_token_ids = processor.tokenizer.all_special_ids + [ processor.tokenizer.word_delimiter_token_id ] # converts id to token, skipping special tokens phonemes = [processor.decode(id_) for id_ in ids if id_ not in special_token_ids] # joins phonemes prediction = " ".join(phonemes) # whether to ignore IPA stress marks if ignore_stress == True: prediction = prediction.replace("ˈ", "").replace("ˌ", "") return prediction checkpoint = "bookbot/wav2vec2-ljspeech-gruut" model = AutoModelForCTC.from_pretrained(checkpoint) processor = AutoProcessor.from_pretrained(checkpoint) sr = processor.feature_extractor.sampling_rate # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") audio_array = ds[0]["audio"]["array"] # or, read a single audio file # audio_array, _ = librosa.load("myaudio.wav", sr=sr) inputs = processor(audio_array, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs["input_values"]).logits predicted_ids = torch.argmax(logits, dim=-1) prediction = decode_phonemes(predicted_ids[0], processor, ignore_stress=True) # => should give 'b ɪ k ʌ z j u ɚ z s l i p ɪ ŋ ɪ n s t ɛ d ə v k ɔ ŋ k ɚ ɪ ŋ ð ə l ʌ v l i ɹ z p ɹ ɪ n s ə s h æ z b ɪ k ʌ m ə v f ɪ t ə l w ɪ θ n b oʊ p ɹ ə ʃ æ ɡ i s ɪ t s ð ɛ ɹ ə k u ɪ ŋ d ʌ v' ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - `learning_rate`: 0.0001 - `train_batch_size`: 16 - `eval_batch_size`: 8 - `seed`: 42 - `gradient_accumulation_steps`: 2 - `total_train_batch_size`: 32 - `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08` - `lr_scheduler_type`: linear - `lr_scheduler_warmup_steps`: 1000 - `num_epochs`: 30.0 - `mixed_precision_training`: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | | :-----------: | :---: | :---: | :-------------: | :----: | :----: | | No log | 1.0 | 348 | 2.2818 | 1.0 | 1.0 | | 2.6692 | 2.0 | 696 | 0.2045 | 0.0527 | 0.0299 | | 0.2225 | 3.0 | 1044 | 0.1162 | 0.0319 | 0.0189 | | 0.2225 | 4.0 | 1392 | 0.0927 | 0.0235 | 0.0147 | | 0.0868 | 5.0 | 1740 | 0.0797 | 0.0218 | 0.0143 | | 0.0598 | 6.0 | 2088 | 0.0715 | 0.0197 | 0.0128 | | 0.0598 | 7.0 | 2436 | 0.0652 | 0.0160 | 0.0103 | | 0.0447 | 8.0 | 2784 | 0.0571 | 0.0152 | 0.0095 | | 0.0368 | 9.0 | 3132 | 0.0608 | 0.0163 | 0.0112 | | 0.0368 | 10.0 | 3480 | 0.0586 | 0.0137 | 0.0083 | | 0.0303 | 11.0 | 3828 | 0.0641 | 0.0141 | 0.0085 | | 0.0273 | 12.0 | 4176 | 0.0656 | 0.0131 | 0.0079 | | 0.0232 | 13.0 | 4524 | 0.0690 | 0.0133 | 0.0082 | | 0.0232 | 14.0 | 4872 | 0.0598 | 0.0128 | 0.0079 | | 0.0189 | 15.0 | 5220 | 0.0671 | 0.0121 | 0.0074 | | 0.017 | 16.0 | 5568 | 0.0654 | 0.0114 | 0.0069 | | 0.017 | 17.0 | 5916 | 0.0751 | 0.0118 | 0.0073 | | 0.0146 | 18.0 | 6264 | 0.0653 | 0.0112 | 0.0068 | | 0.0127 | 19.0 | 6612 | 0.0682 | 0.0112 | 0.0069 | | 0.0127 | 20.0 | 6960 | 0.0678 | 0.0114 | 0.0068 | | 0.0114 | 21.0 | 7308 | 0.0656 | 0.0111 | 0.0066 | | 0.0101 | 22.0 | 7656 | 0.0669 | 0.0109 | 0.0066 | | 0.0092 | 23.0 | 8004 | 0.0677 | 0.0108 | 0.0065 | | 0.0092 | 24.0 | 8352 | 0.0653 | 0.0104 | 0.0063 | | 0.0088 | 25.0 | 8700 | 0.0673 | 0.0102 | 0.0063 | | 0.0074 | 26.0 | 9048 | 0.0669 | 0.0105 | 0.0064 | | 0.0074 | 27.0 | 9396 | 0.0707 | 0.0101 | 0.0061 | | 0.0066 | 28.0 | 9744 | 0.0673 | 0.0100 | 0.0060 | | 0.0058 | 29.0 | 10092 | 0.0689 | 0.0100 | 0.0059 | | 0.0058 | 30.0 | 10440 | 0.0683 | 0.0099 | 0.0058 | ## Disclaimer Do consider the biases which came from pre-training datasets that may be carried over into the results of this model. ## Authors Wav2Vec2 LJSpeech Gruut was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Cloud. ## Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.10.0 - Datasets 2.7.1 - Tokenizers 0.13.2 - Gruut 2.3.4

28281abd2b834ea784f81db1f7b19267

MK096/finetuning-sentiment-model-3000-samples

MK096

distilbert

23

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,055

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2453 - Accuracy: 0.92 - F1: 0.9098 ## 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: 2 ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

a432774109dcc192a4ad56db01b02012

anhtv/distilbert-base-uncased-finetuned-cola

anhtv

distilbert

13

2

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,571

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7992 - Matthews Correlation: 0.5530 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.523 | 1.0 | 535 | 0.5411 | 0.4128 | | 0.3479 | 2.0 | 1070 | 0.5195 | 0.4901 | | 0.2357 | 3.0 | 1605 | 0.5492 | 0.5444 | | 0.1758 | 4.0 | 2140 | 0.7339 | 0.5387 | | 0.1244 | 5.0 | 2675 | 0.7992 | 0.5530 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

891e6a6b6e436c3380d40e2fe105b87c

tomekkorbak/gifted_shirley

tomekkorbak

null

2

0

null

0

null

false

mit

['en']

['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

8,997

false

# gifted_shirley This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 1562 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'every_n_steps': 16, 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'every_n_steps': 16, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0', 'value_head_config': {'is_detached': False}}, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'alpha': 0.5, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'gifted_shirley', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.001, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 1673, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/1rminqjf

d029d1797b63bf4122bb97ecd5a3e495

jonatasgrosman/exp_w2v2t_uk_xlsr-53_s324

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['uk']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'uk']

false

true

461

false

# exp_w2v2t_uk_xlsr-53_s324 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

14b03d0d2f22a6edc4ae8ab591c55de3

gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_cola_256

gokuls

distilbert

17

0

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,744

false

# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_cola_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6912 - Matthews Correlation: 0.1233 ## 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: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6278 | 1.0 | 835 | 0.6912 | 0.1233 | | 0.5109 | 2.0 | 1670 | 0.7554 | 0.1039 | | 0.4467 | 3.0 | 2505 | 0.7497 | 0.1097 | | 0.3975 | 4.0 | 3340 | 0.7609 | 0.1608 | | 0.3601 | 5.0 | 4175 | 0.7996 | 0.1259 | | 0.3298 | 6.0 | 5010 | 0.7797 | 0.1247 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.9.0 - Tokenizers 0.13.2

7f97e265ff429e50a1d5a6167f6d6165

Haakf/distilbert-base-uncased-padded_right_allsides_news

Haakf

distilbert

8

2

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,893

false

# Haakf/distilbert-base-uncased-padded_right_allsides_news This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0256 - Validation Loss: 1.9353 - Epoch: 8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -797, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1715 | 2.0552 | 0 | | 2.1470 | 1.9776 | 1 | | 2.1101 | 1.9531 | 2 | | 2.0782 | 1.9760 | 3 | | 2.0417 | 1.9202 | 4 | | 2.0219 | 1.9425 | 5 | | 2.0121 | 1.9255 | 6 | | 2.0290 | 1.9868 | 7 | | 2.0256 | 1.9353 | 8 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

97dcd72541202454c3a5d0e51e99952c

XLab/rst-sentiment-classification-11b

XLab

t5

6

1

transformers

2

text2text-generation

true

false

afl-3.0

null

0

[]

false

true

11,247

false

<img src="https://expressai-xlab.s3.amazonaws.com/rst/intro_rst.png" width="1000"/> # reStructured Pre-training (RST) official [repository](https://github.com/ExpressAI/reStructured-Pretraining), [paper](https://arxiv.org/pdf/2206.11147.pdf), [easter eggs](http://expressai.co/peripherals/emoji-eng.html) #### RST is a new paradigm for language pre-training, which * unifies **26** different types of signal from **10** data sources (Totten Tomatoes, Dailymail, Wikipedia, Wikidata, Wikihow, Wordnet, arXiv etc ) in the world structurally, being pre-trained with a monolithcal model, * surpasses strong competitors (e.g., T0) on **52/55** popular datasets from a variety of NLP tasks (classification, IE, retrieval, generation etc) * achieves superior performance in National College Entrance Examination **(Gaokao-English, 高考-英语)** achieves **40** points higher than the average scores made by students and 15 points higher than GPT3 with **1/16** parameters. In particular, Qin gets a high score of **138.5** (the full mark is 150) in the 2018 English exam In such a pre-training paradigm, * Data-centric Pre-training: the role of data will be re-emphasized, and model pre-training and fine-tuning of downstream tasks are viewed as a process of data storing and accessing * Pre-training over JSON instead of TEXT: a good storage mechanism should not only have the ability to cache a large amount of data but also consider the ease of access. ## Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b | Trained with all the signals below except signals that are used to train Gaokao models | All applications below （specialized models are recommended first if high performance is preferred） | | rst-fact-retrieval-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym, wikiHow category hierarchy, Wikidata relation, Wikidata entity typing, Paperswithcode entity typing | Knowledge intensive tasks, information extraction tasks,factual checker | | rst-summarization-11b | Trained with the following signals: DailyMail summary, Paperswithcode summary, arXiv summary, wikiHow summary | Summarization or other general generation tasks, meta-evaluation (e.g., BARTScore) | | rst-temporal-reasoning-11b | Trained with the following signals: DailyMail temporal information, wikiHow procedure | Temporal reasoning, relation extraction, event-based extraction | | rst-information-extraction-11b | Trained with the following signals: Paperswithcode entity, Paperswithcode entity typing, Wikidata entity typing, Wikidata relation, Wikipedia entity | Named entity recognition, relation extraction and other general IE tasks in the news, scientific or other domains| | rst-intent-detection-11b | Trained with the following signals: wikiHow goal-step relation | Intent prediction, event prediction | | rst-topic-classification-11b | Trained with the following signals: DailyMail category, arXiv category, wikiHow text category, Wikipedia section title | general text classification | | rst-word-sense-disambiguation-11b | Trained with the following signals: WordNet meaning, WordNet part-of-speech, WordNet synonym, WordNet antonym | Word sense disambiguation, part-of-speech tagging, general IE tasks, common sense reasoning | | rst-natural-language-inference-11b | Trained with the following signals: ConTRoL dataset, DREAM dataset, LogiQA dataset, RACE & RACE-C dataset, ReClor dataset, DailyMail temporal information | Natural language inference, multiple-choice question answering, reasoning | | **rst-sentiment-classification-11b** | **Trained with the following signals: Rotten Tomatoes sentiment, Wikipedia sentiment** | **Sentiment classification, emotion classification** | | rst-gaokao-rc-11b | Trained with multiple-choice QA datasets that are used to train the [T0pp](https://huggingface.co/bigscience/T0pp) model | General multiple-choice question answering| | rst-gaokao-cloze-11b | Trained with manually crafted cloze datasets | General cloze filling| | rst-gaokao-writing-11b | Trained with example essays from past Gaokao-English exams and grammar error correction signals | Essay writing, story generation, grammar error correction and other text generation tasks | ## Have a try? ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("XLab/rst-all-11b") model = AutoModelForSeq2SeqLM.from_pretrained("XLab/rst-all-11b") inputs = tokenizer.encode("TEXT: this is the best cast iron skillet you will ever buy. QUERY: Is this review \"positive\" or \"negative\"", return_tensors="pt") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)) ``` ## Data for reStructure Pre-training This dataset is a precious treasure, containing a variety of naturally occurring signals. Any downstream task you can think of (e.g., the college entrance exam mentioned in the RST paper) can benefit from being pre-trained on some of our provided signals. We spent several months collecting the following 29 signal types, accounting for a total of 46,926,447 data samples. We hope this dataset will be a valuable asset for everyone in natural language processing research. We provide collected signals through [DataLab](https://github.com/ExpressAI/DataLab). For efficiency, we only provide 50,000 samples at most for each signal type. If you want all the samples we collected, please fill this [form](https://docs.google.com/forms/d/e/1FAIpQLSdPO50vSdfwoO3D7DQDVlupQnHgrXrwfF3ePE4X1H6BwgTn5g/viewform?usp=sf_link). More specifically, we collected the following signals. ###### We will be happy :smiley: to know if the resource is helpful for your work, and please cite our [work](https://github.com/ExpressAI/reStructured-Pretraining/blob/main/README.md#Bib) :blush: | Mine | Signal | #Sample | Use in DataLab | Some Applications | | --- | --- | --- | --- | --- | | [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, category) | 899,904 | `load_dataset("rst", "daily_mail_category")`| Topic classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (title, text, summary) | 1,026,616 | `load_dataset("rst", "daily_mail_summary")` | Summarization; Sentence expansion| | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, events) | 1,006,412 | `load_dataset("rst", "daily_mail_temporal")` | Temporal reasoning| | [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (entity, entity_type, text) | 2,214,274 | `load_dataset("rst", "wikidata_entity")` | Entity typing| | [Wikidata](https://www.wikidata.org/wiki/Wikidata:Main_Page) | (subject, object, relation, text) | 1,526,674 | `load_dataset("rst", "wikidata_relation")` | Relation extraction; Fact retrieval| | [wikiHow](https://www.wikihow.com/Main-Page) | (text, category) | 112,109 | `load_dataset("rst", "wikihow_text_category")` | Topic classification | | [wikiHow](https://www.wikihow.com/Main-Page) | (low_category, high_category) | 4,868 | `load_dataset("rst", "wikihow_category_hierarchy")` | Relation extraction; Commonsense reasoning| | [wikiHow](https://www.wikihow.com/Main-Page) | (goal, steps) | 47,956 | `load_dataset("rst", "wikihow_goal_step")` | Intent detection| | [wikiHow](https://www.wikihow.com/Main-Page) | (text, summary) | 703,278 | `load_dataset("rst", "wikihow_summary")` | Summarization; Sentence expansion | | [wikiHow](https://www.wikihow.com/Main-Page) | (goal, first_step, second_step) | 47,787 | `load_dataset("rst", "wikihow_procedure")` | Temporal reasoning | | [wikiHow](https://www.wikihow.com/Main-Page) | (question, description, answer, related_questions) | 47,705 | `load_dataset("rst", "wikihow_question")` | Question generation| | [Wikipedia](https://www.wikipedia.org/) | (text, entities) |22,231,011 | `load_dataset("rst", "wikipedia_entities")` | Entity recognition| [Wikipedia](https://www.wikipedia.org/) | (texts, titles) | 3,296,225 | `load_dataset("rst", "wikipedia_sections")` | Summarization| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, pos) | 27,123 | `load_dataset("rst", "wordnet_pos")` | Part-of-speech tagging| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, meaning, possible_meanings) | 27,123 | `load_dataset("rst", "wordnet_meaning")` | Word sense disambiguation| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, synonyms) | 17,804 | `load_dataset("rst", "wordnet_synonym")`| Paraphrasing| | [WordNet](https://wordnet.princeton.edu/) | (word, sentence, antonyms) | 6,408 | `load_dataset("rst", "wordnet_antonym")` |Negation | | [ConTRoL]() | (premise, hypothesis, label) | 8,323 | `load_dataset("rst", "qa_control")` | Natural language inference| |[DREAM](https://transacl.org/ojs/index.php/tacl/article/view/1534)| (context, question, options, answer) | 9,164 | `load_dataset("rst", "qa_dream")` | Reading comprehension| | [LogiQA](https://doi.org/10.24963/ijcai.2020/501) | (context, question, options, answer) | 7,974 | `load_dataset("rst", "qa_logiqa")` | Reading comprehension| | [ReClor](https://openreview.net/forum?id=HJgJtT4tvB) | (context, question, options, answer) | 5,138 | `load_dataset("rst", "qa_reclor")` |Reading comprehension | | [RACE](https://doi.org/10.18653/v1/d17-1082) | (context, question, options, answer) | 44,880 | `load_dataset("rst", "qa_race")` | Reading comprehension| | [RACE-C](http://proceedings.mlr.press/v101/liang19a.html) | (context, question, options, answer) | 5,093 | `load_dataset("rst", "qa_race_c")` | Reading comprehension| | [TriviaQA](https://doi.org/10.18653/v1/P17-1147) | (context, question, answer) | 46,636 | `load_dataset("rst", "qa_triviaqa")` |Reading comprehension | | [Arxiv](https://arxiv.org/) | (text, category) | 1,696,348 | `load_dataset("rst", "arxiv_category")` |Topic classification| | [Arxiv](https://arxiv.org/) | (text, summary) | 1,696,348 | `load_dataset("rst", "arxiv_summary")` | Summarization; Sentence expansion| | [Paperswithcode](https://paperswithcode.com/) | (text, entities, datasets, methods, tasks, metrics) | 4,731,233 | `load_dataset("rst", "paperswithcode_entity")` | Entity recognition| | [Paperswithcode](https://paperswithcode.com/) | (text, summary) | 120,924 | `load_dataset("rst", "paperswithcode_summary")` | Summarization; Sentence expansion| ## Bibtext for Citation Info ``` @article{yuan2022restructured, title={reStructured Pre-training}, author={Yuan, Weizhe and Liu, Pengfei}, journal={arXiv preprint arXiv:2206.11147}, year={2022} } ```

fcfd1e5ec3fbcd1cec2452260b89f125

yasu320001/xlm-roberta-base-finetuned-panx-all

yasu320001

xlm-roberta

10

3

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,319

false

# xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1656 - F1: 0.8589 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2905 | 1.0 | 715 | 0.1783 | 0.8310 | | 0.1461 | 2.0 | 1430 | 0.1600 | 0.8455 | | 0.0948 | 3.0 | 2145 | 0.1656 | 0.8589 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

b40cc788b02505378c0968c2892433e8

julien-c/kan-bayashi-jsut_tts_train_tacotron2

julien-c

null

17

3

espnet

0

text-to-speech

false

cc-by-4.0

['ja']

['jsut']

null

0

['espnet', 'audio', 'text-to-speech']

false

true

1,903

false

## Example ESPnet2 TTS model ### `kan-bayashi/jsut_tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4381098/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Training ![](./exp/tts_train_tacotron2_raw_phn_jaconv_pyopenjtalk_accent/images/attn_loss.png) ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

3be44f9755f55eea813472fbb092214a

Helsinki-NLP/opus-mt-zlw-fiu

Helsinki-NLP

marian

12

9

transformers

0

translation

true

false

apache-2.0

['dsb', 'cs', 'csb_Latn', 'hsb', 'pl', 'zlw', 'hu', 'vro', 'fi', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'et', 'sma', 'udm', 'vep', 'myv', 'kpv', 'se', 'izh', 'fiu']

null

0

['translation']

false

true

3,471

false

### zlw-fiu * source language name: West Slavic languages * target language name: Finno-Ugrian languages * OPUS readme: [README.md](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/README.md) * model: transformer * source language codes: dsb, cs, csb_Latn, hsb, pl, zlw * target language codes: hu, vro, fi, liv_Latn, mdf, krl, fkv_Latn, mhr, et, sma, udm, vep, myv, kpv, se, izh, fiu * dataset: opus * release date: 2021-02-18 * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2021-02-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/zlw-fiu/opus-2021-02-18.zip) * a sentence-initial language token is required in the form of >>id<<(id = valid, usually three-letter target language ID) * Training data: * ces-fin: Tatoeba-train (1000000) * ces-hun: Tatoeba-train (1000000) * pol-est: Tatoeba-train (1000000) * pol-fin: Tatoeba-train (1000000) * pol-hun: Tatoeba-train (1000000) * Validation data: * ces-fin: Tatoeba-dev, 1000 * ces-hun: Tatoeba-dev, 1000 * est-pol: Tatoeba-dev, 1000 * fin-pol: Tatoeba-dev, 1000 * hun-pol: Tatoeba-dev, 1000 * mhr-pol: Tatoeba-dev, 461 * total-size-shuffled: 5426 * devset-selected: top 5000 lines of Tatoeba-dev.src.shuffled! * Test data: * newssyscomb2009.ces-hun: 502/9733 * newstest2009.ces-hun: 2525/54965 * Tatoeba-test.ces-fin: 88/408 * Tatoeba-test.ces-hun: 1911/10336 * Tatoeba-test.multi-multi: 4562/25497 * Tatoeba-test.pol-chm: 5/36 * Tatoeba-test.pol-est: 15/98 * Tatoeba-test.pol-fin: 609/3293 * Tatoeba-test.pol-hun: 1934/11285 * test set translations file: [test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/zlw-fiu/opus-2021-02-18.test.txt) * test set scores file: [eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/zlw-fiu/opus-2021-02-18.eval.txt) * BLEU-scores |Test set|score| |---|---| |Tatoeba-test.ces-fin|57.2| |Tatoeba-test.ces-hun|42.6| |Tatoeba-test.multi-multi|39.4| |Tatoeba-test.pol-hun|36.6| |Tatoeba-test.pol-fin|36.1| |Tatoeba-test.pol-est|20.9| |newssyscomb2009.ces-hun|13.9| |newstest2009.ces-hun|13.9| |Tatoeba-test.pol-chm|2.0| * chr-F-scores |Test set|score| |---|---| |Tatoeba-test.ces-fin|0.71| |Tatoeba-test.ces-hun|0.637| |Tatoeba-test.multi-multi|0.616| |Tatoeba-test.pol-hun|0.605| |Tatoeba-test.pol-fin|0.592| |newssyscomb2009.ces-hun|0.449| |newstest2009.ces-hun|0.443| |Tatoeba-test.pol-est|0.372| |Tatoeba-test.pol-chm|0.007| ### System Info: * hf_name: zlw-fiu * source_languages: dsb,cs,csb_Latn,hsb,pl,zlw * target_languages: hu,vro,fi,liv_Latn,mdf,krl,fkv_Latn,mhr,et,sma,udm,vep,myv,kpv,se,izh,fiu * opus_readme_url: https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-fiu/opus-2021-02-18.zip/README.md * original_repo: Tatoeba-Challenge * tags: ['translation'] * languages: ['dsb', 'cs', 'csb_Latn', 'hsb', 'pl', 'zlw', 'hu', 'vro', 'fi', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'et', 'sma', 'udm', 'vep', 'myv', 'kpv', 'se', 'izh', 'fiu'] * src_constituents: ['dsb', 'ces', 'csb_Latn', 'hsb', 'pol'] * tgt_constituents: ['hun', 'vro', 'fin', 'liv_Latn', 'mdf', 'krl', 'fkv_Latn', 'mhr', 'est', 'sma', 'udm', 'vep', 'myv', 'kpv', 'sme', 'izh'] * src_multilingual: True * tgt_multilingual: True * helsinki_git_sha: a0966db6db0ae616a28471ff0faf461b36fec07d * transformers_git_sha: 3857f2b4e34912c942694489c2b667d9476e55f5 * port_machine: bungle * port_time: 2021-06-29-15:24

d32abd34e24a214c6df0bc3ed4967e8f

Helsinki-NLP/opus-mt-en-de

Helsinki-NLP

marian

12

147,323

transformers

9

translation

true

cc-by-4.0

null

2

0

2

0

1

0

1

['translation']

false

true

3,263

false

### opus-mt-en-de ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation - **Language(s):** - Source Language: English - Target Language: German - **License:** CC-BY-4.0 - **Resources for more information:** - [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Uses #### Direct Use This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Further details about the dataset for this model can be found in the OPUS readme: [en-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-de/README.md) #### Training Data ##### Preprocessing * pre-processing: normalization + SentencePiece * dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) * download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.zip) * test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.test.txt) ## Evaluation #### Results * test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-de/opus-2020-02-26.eval.txt) #### Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009.en.de | 23.5 | 0.540 | | news-test2008.en.de | 23.5 | 0.529 | | newstest2009.en.de | 22.3 | 0.530 | | newstest2010.en.de | 24.9 | 0.544 | | newstest2011.en.de | 22.5 | 0.524 | | newstest2012.en.de | 23.0 | 0.525 | | newstest2013.en.de | 26.9 | 0.553 | | newstest2015-ende.en.de | 31.1 | 0.594 | | newstest2016-ende.en.de | 37.0 | 0.636 | | newstest2017-ende.en.de | 29.9 | 0.586 | | newstest2018-ende.en.de | 45.2 | 0.690 | | newstest2019-ende.en.de | 40.9 | 0.654 | | Tatoeba.en.de | 47.3 | 0.664 | ## Citation Information ```bibtex @InProceedings{TiedemannThottingal:EAMT2020, author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, year = {2020}, address = {Lisbon, Portugal} } ``` ## How to Get Started With the Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de") ```

3349a9b424364b03df86ff3f6db70e6d

Helsinki-NLP/opus-mt-bem-en

Helsinki-NLP

marian

10

13

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-bem-en * source languages: bem * target languages: en * OPUS readme: [bem-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bem-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/bem-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bem-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bem.en | 33.4 | 0.491 |

66b4f9b2f6185b2cfd2320470fd365eb

sd-concepts-library/rd-chaos

sd-concepts-library

null

9

0

null

0

null

false

mit

null

0

[]

false

true

1,008

false

### RD chaos on Stable Diffusion This is the `<rd-chaos>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<rd-chaos> 0](https://huggingface.co/sd-concepts-library/rd-chaos/resolve/main/concept_images/3.jpeg) ![<rd-chaos> 1](https://huggingface.co/sd-concepts-library/rd-chaos/resolve/main/concept_images/0.jpeg) ![<rd-chaos> 2](https://huggingface.co/sd-concepts-library/rd-chaos/resolve/main/concept_images/1.jpeg) ![<rd-chaos> 3](https://huggingface.co/sd-concepts-library/rd-chaos/resolve/main/concept_images/2.jpeg)

fbdd2647aa22012c596cfa2fdd2b7dfe

Sushant45/Pub-clustered

Sushant45

distilbert

8

24

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,858

false

# Sushant45/Pub-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3589 - Train End Logits Accuracy: 0.8889 - Train Start Logits Accuracy: 0.8924 - Validation Loss: 0.4049 - Validation End Logits Accuracy: 0.9231 - Validation Start Logits Accuracy: 0.9231 - Epoch: 0 ## 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': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3589 | 0.8889 | 0.8924 | 0.4049 | 0.9231 | 0.9231 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

4a0bb1026f005adee00c5ef975f12aa4

doyoungkim/bert-base-uncased-sst2-distilled

doyoungkim

bert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

false

true

1,458

false

# bert-base-uncased-sst2-distilled This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.2676 - Accuracy: 0.9025 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3797 | 1.0 | 2105 | 0.2512 | 0.9002 | | 0.3036 | 2.0 | 4210 | 0.2643 | 0.8933 | | 0.2609 | 3.0 | 6315 | 0.2831 | 0.8956 | | 0.2417 | 4.0 | 8420 | 0.2676 | 0.9025 | | 0.2305 | 5.0 | 10525 | 0.2740 | 0.9025 | ### Framework versions - Transformers 4.9.1 - Pytorch 1.8.1 - Datasets 1.11.0 - Tokenizers 0.10.1

67005e8ff25b177aa581c743aed8d631

vumichien/whisper-medium-mix-jp-ver2

vumichien

whisper

22

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,971

false

# openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2790 - Wer: 8.3986 - Cer: 5.2582 ## 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: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:| | 0.1691 | 1.01 | 1000 | 0.1871 | 10.1740 | 6.3509 | | 0.0916 | 2.02 | 2000 | 0.1691 | 8.9797 | 5.5499 | | 0.0452 | 3.03 | 3000 | 0.1902 | 8.9814 | 5.5867 | | 0.0213 | 4.04 | 4000 | 0.2062 | 8.9375 | 5.6531 | | 0.0096 | 5.05 | 5000 | 0.2284 | 8.7331 | 5.6202 | | 0.0041 | 6.05 | 6000 | 0.2395 | 8.5051 | 5.3009 | | 0.0022 | 7.06 | 7000 | 0.2535 | 8.5507 | 5.3640 | | 0.001 | 8.07 | 8000 | 0.2656 | 8.5557 | 5.3791 | | 0.0006 | 9.08 | 9000 | 0.2721 | 8.4037 | 5.2739 | | 0.0004 | 10.09 | 10000 | 0.2790 | 8.3986 | 5.2582 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

ae18c9e98fa8a7984f72e60e1f4276b9

Helsinki-NLP/opus-mt-de-pag

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-de-pag * source languages: de * target languages: pag * OPUS readme: [de-pag](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-pag/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-pag/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-pag/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-pag/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.de.pag | 24.3 | 0.469 |

e7ef6277167414aa2d682e6751f49eae

Aldraz/distilbert-base-uncased-finetuned-emotion

Aldraz

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,339

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2319 - Accuracy: 0.921 - F1: 0.9214 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3369 | 0.8985 | 0.8947 | | No log | 2.0 | 500 | 0.2319 | 0.921 | 0.9214 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1+cpu - Datasets 2.1.0 - Tokenizers 0.11.6

bb04fc9bfab40f6c1b10b0bec9c105af

jonatasgrosman/exp_w2v2t_ja_vp-es_s673

jonatasgrosman

wav2vec2

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ja']

false

true

469

false

# exp_w2v2t_ja_vp-es_s673 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

bd5691055bd1ccb45d6290726e073e61

robertou2/roberta-base-bne-finetuned-amazon_reviews_multi

robertou2

roberta

13

1

transformers

0

text-classification

true

false

apache-2.0

null

['amazon_reviews_multi']

null

0

['generated_from_trainer']

true

1,346

false

# roberta-base-bne-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2368 - Accuracy: 0.9325 ## Model description Modelo de prueba del curso NLP de 0 a 100 sesion 4 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1919 | 1.0 | 1250 | 0.1690 | 0.933 | | 0.0972 | 2.0 | 2500 | 0.2368 | 0.9325 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6

22d6e31ce63302c720ff28c6fd1e0c13

espnet/ftshijt_mls_asr_transformer_valid.acc.best

espnet

null

31

3

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['es']

['mls']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

1,805

false

## Example ESPnet2 ASR model ### `ftshijt/mls_asr_transformer_valid.acc.best` ♻️ Imported from https://zenodo.org/record/4458452/ This model was trained by ftshijt using mls/asr1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

2ec5a13d6a1504315ff87a708628d1d8

mariagrandury/roberta-base-finetuned-sms-spam-detection

mariagrandury

roberta

13

333

transformers

2

text-classification

true

false

mit

null

['sms_spam']

null

0

['generated_from_trainer']

true

1,274

false

# roberta-base-finetuned-sms-spam-detection This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sms_spam dataset. It achieves the following results on the evaluation set: - Loss: 0.0133 - Accuracy: 0.998 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0363 | 1.0 | 250 | 0.0156 | 0.996 | | 0.0147 | 2.0 | 500 | 0.0133 | 0.998 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

80c106671253535fbeadaad7b3cab90d

anas-awadalla/gpt2-span-head-few-shot-k-512-finetuned-squad-seed-4

anas-awadalla

gpt2

20

5

transformers

0

question-answering

true

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

966

false

# gpt2-span-head-few-shot-k-512-finetuned-squad-seed-4 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the squad 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: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6

9206421965902a33ca5564a0cfef8685

mrojas/roberta-clinical-wl-es-finetuned-ner

mrojas

roberta

14

8

transformers

0

token-classification

true

false

apache-2.0

null

['wl']

null

0

['generated_from_trainer']

true

1,559

false

# roberta-clinical-wl-es-finetuned-ner This model is a fine-tuned version of [plncmm/roberta-clinical-wl-es](https://huggingface.co/plncmm/roberta-clinical-wl-es) on the wl dataset. It achieves the following results on the evaluation set: - Loss: 0.6227 - Precision: 0.6865 - Recall: 0.7355 - F1: 0.7102 - Accuracy: 0.8268 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.028 | 1.0 | 500 | 0.6870 | 0.6558 | 0.6855 | 0.6703 | 0.8035 | | 0.5923 | 2.0 | 1000 | 0.6248 | 0.6851 | 0.7235 | 0.7038 | 0.8244 | | 0.4928 | 3.0 | 1500 | 0.6227 | 0.6865 | 0.7355 | 0.7102 | 0.8268 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

fcd5e320e441f696671b369b980f379b

RANG012/SENATOR

RANG012

distilbert

13

6

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

1

0

['generated_from_trainer']

true

1,022

false

# SENATOR This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2707 - Accuracy: 0.916 - F1: 0.9167 ## 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: 2 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

fc98eb5d4a4703c0179b8cbbb6029114

dfurman/Swin-base-chesapeake-land-cover-v0

dfurman

swin

14

7

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['image-classification', 'generated_from_trainer']

true

1,436

false

# Swin-base-chesapeake-land-cover-v0 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0430 - Accuracy: 0.9899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0326 | 1.15 | 100 | 0.1309 | 0.9588 | | 0.0102 | 2.3 | 200 | 0.0430 | 0.9899 | | 0.0082 | 3.45 | 300 | 0.0466 | 0.9914 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

873981c1da3378cb5cc8c84e85cb567d

explosion/vi_udv25_vietnamesevtb_trf

explosion

null

28

2

spacy

0

token-classification

false

cc-by-sa-4.0

['vi']

null

0

['spacy', 'token-classification']

false

true

2,085

false

UD v2.5 benchmarking pipeline for UD_Vietnamese-VTB | Feature | Description | | --- | --- | | **Name** | `vi_udv25_vietnamesevtb_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (81 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `!`, `"`, `,`, `-`, `.`, `...`, `:`, `;`, `?`, `@`, `A`, `C`, `CC`, `E`, `I`, `L`, `LBKT`, `M`, `N`, `NP`, `Nb`, `Nc`, `Np`, `Nu`, `Ny`, `P`, `R`, `RBKT`, `T`, `V`, `VP`, `X`, `Y`, `Z` | | **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=X\|Polarity=Neg`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=X`, `POS=SCONJ`, `NumType=Card\|POS=NUM`, `POS=DET`, `POS=CCONJ`, `POS=PROPN`, `POS=AUX`, `POS=PART`, `POS=INTJ` | | **`parser`** | `ROOT`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `0` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 87.90 | | `TOKEN_P` | 86.84 | | `TOKEN_R` | 89.00 | | `TOKEN_ACC` | 98.42 | | `SENTS_F` | 94.33 | | `SENTS_P` | 96.23 | | `SENTS_R` | 92.50 | | `TAG_ACC` | 88.05 | | `POS_ACC` | 90.19 | | `MORPH_ACC` | 96.95 | | `DEP_UAS` | 68.08 | | `DEP_LAS` | 60.64 | | `LEMMA_ACC` | 89.35 |

21ad063c6f1a6f4fce967aec44a20ae2

Meow412/finetuning-sentiment-model-A3

Meow412

distilbert

13

5

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,045

false

# finetuning-sentiment-model-A3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3212 - Accuracy: 0.8760 - F1: 0.3516 ## 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: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

c8aebbdc342ed781d4ba3c705a2111c0

PaddlePaddle/ernie-2.0-large-zh

PaddlePaddle

ernie

7

0

paddlenlp

0

null

false

apache-2.0

['zh']

null

0

[]

false

true

1,590

false

# PaddlePaddle/ernie-2.0-large-zh ## Introduction Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring, there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness and discourse relations. In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0 which builds and learns incrementally pre-training tasks through constant multi-task learning. Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese. More detail: https://arxiv.org/abs/1907.12412 ## Available Models - ernie-2.0-base-en - ernie-2.0-large-en - ernie-2.0-base-zh - ernie-2.0-large-zh ## How to Use? Click on the *Use in paddlenlp* button on the top right! ## Citation Info ```text @article{ernie2.0, title = {ERNIE 2.0: A Continual Pre-training Framework for Language Understanding}, author = {Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:1907.12412}, year = {2019}, } ```

442869cbd7f31d29188e3be2e5b9040c

HyperMoon/wav2vec2-base-finetuned-deepfake-0919

HyperMoon

wav2vec2

10

12

transformers

0

audio-classification

true

false

apache-2.0

null

['asvspoof2019']

null

0

['generated_from_trainer']

true

1,574

false

# wav2vec2-base-finetuned-deepfake-0919 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the asvspoof2019 dataset. It achieves the following results on the evaluation set: - Loss: 0.3335 - Accuracy: 0.8974 ## 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3025 | 1.0 | 1586 | 0.3335 | 0.8974 | | 0.4214 | 2.0 | 3172 | 0.3331 | 0.8974 | | 0.4378 | 3.0 | 4758 | 0.3307 | 0.8974 | | 0.3993 | 4.0 | 6344 | 0.3331 | 0.8974 | | 0.2839 | 5.0 | 7930 | 0.3315 | 0.8974 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

24043b57b27f66a03239031e805ef5ab

commanderstrife/ADE-Bio_ClinicalBERT-NER

commanderstrife

bert

12

7

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,739

false

# ADE-Bio_ClinicalBERT-NER This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1926 - Precision: 0.7830 - Recall: 0.8811 - F1: 0.8291 - Accuracy: 0.9437 ## 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2389 | 1.0 | 201 | 0.2100 | 0.7155 | 0.8292 | 0.7681 | 0.9263 | | 0.0648 | 2.0 | 402 | 0.1849 | 0.7716 | 0.8711 | 0.8183 | 0.9392 | | 0.2825 | 3.0 | 603 | 0.1856 | 0.7834 | 0.8788 | 0.8284 | 0.9422 | | 0.199 | 4.0 | 804 | 0.1875 | 0.7796 | 0.8781 | 0.8259 | 0.9430 | | 0.0404 | 5.0 | 1005 | 0.1926 | 0.7830 | 0.8811 | 0.8291 | 0.9437 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

58f04d17b09f3258645dcdeeeeb2b8d0

spacy/es_core_news_lg

spacy

null

28

38

spacy

1

token-classification

false

gpl-3.0

['es']

null

0

['spacy', 'token-classification']

false

true

29,913

false

### Details: https://spacy.io/models/es#es_core_news_lg Spanish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `es_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) | | **Sources** | [UD Spanish AnCora v2.8](https://github.com/UniversalDependencies/UD_Spanish-AnCora) (Martínez Alonso, Héctor; Zeman, Daniel) [WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran) [spaCy lookups data](https://github.com/explosion/spacy-lookups-data) (Explosion) [Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) | | **License** | `GNU GPL 3.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (468 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=ADP`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PROPN`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PRON\|PronType=Int,Rel`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `POS=SCONJ`, `POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Number=Plur\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=PUNCT\|PunctType=Peri`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Comm`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=ADV`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `POS=PRON\|PronType=Ind`, `POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `POS=PUNCT\|PunctType=Quot`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumType=Card\|POS=NUM`, `POS=VERB\|VerbForm=Ger`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADV`, `POS=AUX\|VerbForm=Inf`, `Number=Plur\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Dem`, `POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `AdvType=Tim\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `NumForm=Digit\|POS=NOUN`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|POS=NOUN`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `AdvType=Tim\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `POS=SPACE`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PUNCT\|PunctType=Colo`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PUNCT\|PunctType=Semi`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `POS=PUNCT\|PunctType=Dash`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|POS=NOUN`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=NOUN\|VerbForm=Inf`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `POS=DET\|PronType=Ind`, `POS=DET\|PronType=Int,Rel`, `AdvType=Tim\|POS=ADV`, `POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Degree=Abs\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Case=Dat\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SCONJ\|PronType=Int,Rel`, `Case=Acc,Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc,Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=SYM`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Com\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=NOUN\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Mood=Imp\|Number=Plur,Sing\|POS=VERB\|Person=1,2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Number=Sing\|POS=VERB\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Abs\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Fin`, `POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Acc,Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Acc,Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Definite=Def\|Foreign=Yes\|POS=DET\|PronType=Art`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|PunctType=Quot\|VerbForm=Inf`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Com\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Acc,Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=PRON\|PronType=Dem`, `Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `POS=AUX\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc,Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `AdvType=Tim\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN\|VerbForm=Part`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Case=Acc,Dat\|Number=Plur\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Com\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `POS=X`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Case=Com\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Case=Acc,Dat\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Number=Sing\|POS=AUX\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Case=Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=1,3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Inf`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=NOUN\|PunctType=Comm`, `Degree=Cmp\|POS=ADJ`, `Gender=Masc\|POS=ADJ`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `POS=PRON\|PronType=Neg`, `Case=Acc,Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes\|VerbForm=Ger`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PrepCase=Npr\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Int,Rel`, `Definite=Def\|Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADP`, `Foreign=Yes\|POS=CCONJ`, `Foreign=Yes\|POS=PROPN` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:impers`, `expl:pass`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 99.89 | | `TOKEN_R` | 99.95 | | `TOKEN_F` | 99.92 | | `POS_ACC` | 98.51 | | `MORPH_ACC` | 98.19 | | `MORPH_MICRO_P` | 99.56 | | `MORPH_MICRO_R` | 98.98 | | `MORPH_MICRO_F` | 99.27 | | `SENTS_P` | 96.90 | | `SENTS_R` | 98.50 | | `SENTS_F` | 97.70 | | `DEP_UAS` | 91.40 | | `DEP_LAS` | 88.19 | | `TAG_ACC` | 96.14 | | `LEMMA_ACC` | 96.58 | | `ENTS_P` | 89.67 | | `ENTS_R` | 89.78 | | `ENTS_F` | 89.72 |

5f9542c3e3a9fc63cd00b1b3d4eca7e9

susnato/xlm-roberta-base-finetuned-panx-it

susnato

xlm-roberta

9

12

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,317

false

# xlm-roberta-base-finetuned-panx-it 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: 0.3544 - F1: 0.8235 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7074 | 1.0 | 210 | 0.4237 | 0.7311 | | 0.3172 | 2.0 | 420 | 0.3662 | 0.7820 | | 0.1855 | 3.0 | 630 | 0.3544 | 0.8235 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

6ccf9c659b4c1194aa6361ac89e2f69d

anas-awadalla/albert-xxl-v2-finetuned-squad

anas-awadalla

albert

16

3

transformers

1

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

960

false

# albert-xxl-v2-finetuned-squad This model is a fine-tuned version of [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2) on the squad 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

eac60fc8c8007539bbef8a54e45c3d87

GItaf/roberta-base-roberta-base-finetuned-mbti-0911

GItaf

roberta

13

2

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,079

false

# roberta-base-roberta-base-finetuned-mbti-0911 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 4.1338 - eval_runtime: 25.7058 - eval_samples_per_second: 67.495 - eval_steps_per_second: 8.442 - step: 0 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

00675fe601a9bd00daa8e01bd176f50b

Helsinki-NLP/opus-mt-lua-fr

Helsinki-NLP

marian

10

20

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-lua-fr * source languages: lua * target languages: fr * OPUS readme: [lua-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lua-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lua-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-fr/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lua.fr | 25.7 | 0.429 |

cbfdd14a9d4fcbd38c473db0db3d9918

nestoralvaro/mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base

nestoralvaro

mt5

12

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,477

false

# mt5-base-finetuned-xsum-data_prep_2021_12_26___t1_7.csv___topic_text_google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 2.8146 - Rouge2: 0.6707 - Rougel: 2.8187 - Rougelsum: 2.8098 - Gen Len: 6.4901 ## 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: 4 - eval_batch_size: 4 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 3869 | nan | 2.8146 | 0.6707 | 2.8187 | 2.8098 | 6.4901 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

d9dc50c20ce5b69307b0a49ae972e53b

FolkFoxWalker/my_awesome_billsum_model

FolkFoxWalker

t5

11

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['billsum']

null

0

['generated_from_trainer']

true

1,703

false

# my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5057 - Rouge1: 0.1437 - Rouge2: 0.0544 - Rougel: 0.12 - Rougelsum: 0.12 - Gen Len: 19.0 ## 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: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.7978 | 0.1276 | 0.039 | 0.1077 | 0.1076 | 19.0 | | No log | 2.0 | 124 | 2.5889 | 0.1371 | 0.0489 | 0.1153 | 0.1151 | 19.0 | | No log | 3.0 | 186 | 2.5234 | 0.1429 | 0.054 | 0.1196 | 0.1194 | 19.0 | | No log | 4.0 | 248 | 2.5057 | 0.1437 | 0.0544 | 0.12 | 0.12 | 19.0 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

073bde416531cf0f13ed48f2794cdfd6

mabaji/thepoet

mabaji

gpt2

8

15

transformers

0

text-generation

true

false

apache-2.0

['ar']

null

0

['text-generation']

false

true

724

false

Thepoet is an Arabic poem generator, pre-trained language model based on OpenAi GPT2 architechture. Special thanks to aubmindlab for their pretrained Arabic model - Aragpt2 - large (https://huggingface.co/aubmindlab/aragpt2-large) AraGPT2-large adafactor 1024 1280 20 36 2.98GB/792M Trained on two huge (APCD) datasets: 512MB Arabic Poem Comprehensive Dataset from Kaggle (https://www.kaggle.com/datasets/mohamedkhaledelsafty/best-arabic-poem-comprehensive-dataset) 150MB Arabic Poem Dataset from Kaggle(https://www.kaggle.com/datasets/ahmedabelal/arabic-poetry) ## Eval results Final perplexity reached was 119.5661 ### BibTeX entry and citation info ```bibtex @inproceedings{Mohamad El Abaji, year={2022} } ```

6cb93547fc78a4925d75ca36ced9d480

Geotrend/distilbert-base-tr-cased

Geotrend

distilbert

6

5

transformers

0

fill-mask

true

false

apache-2.0

['tr']

['wikipedia']

null

0

[]

false

true

1,215

false

# distilbert-base-tr-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-tr-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-tr-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.

9d5de1a7d32d081c9bb24dbd9f835c39

alexamiredjibi/Multimodal-Trajectory-Classifier-30

alexamiredjibi

distilbert

8

0

transformers

0

null

true

false

apache-2.0

null

0

['generated_from_trainer']

true

997

false

# Multimodal-Trajectory-Classifier-30 This model is a fine-tuned version of [alexamiredjibi/Multimodal-Trajectory-Classifier](https://huggingface.co/alexamiredjibi/Multimodal-Trajectory-Classifier) 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

d8a3d887f1390f26860a1bd6f9a8bc38

tommy19970714/wav2vec2-base-960h

tommy19970714

wav2vec2

7

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['librispeech_asr']

null

0

['audio', 'automatic-speech-recognition']

false

true

3,629

false

# Wav2Vec2-Base-960h This repository is a reimplementation of [official Facebook’s wav2vec](https://huggingface.co/facebook/wav2vec2-base-960h). There is no description of converting the wav2vec [pretrain model](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20) to a pytorch.bin file. We are rebuilding pytorch.bin from the pretrain model. Here is the conversion method. ```bash pip install transformers[sentencepiece] pip install fairseq -U git clone https://github.com/huggingface/transformers.git cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py . wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small_960h.pt -O ./wav2vec_small_960h.pt mkdir dict wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt mkdir outputs python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py --pytorch_dump_folder_path ./outputs --checkpoint_path ./wav2vec_small_960h.pt --dict_path ./dict ``` # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC from datasets import load_dataset import soundfile as sf import torch # load model and tokenizer tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") # define function to read in sound file def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.map(map_to_array) # tokenize input_values = tokenizer(ds["speech"][:2], return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = tokenizer.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-base-960h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer import soundfile as sf import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch librispeech_eval = librispeech_eval.map(map_to_array) def map_to_pred(batch): input_values = tokenizer(batch["speech"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = tokenizer.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 3.4 | 8.6 | # Reference [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) [Facebook's huggingface Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h) [Paper](https://arxiv.org/abs/2006.11477)

c72a1eb8a21cde8ca1214642546bd9fd

wolinski/constituency-brackets-20

wolinski

bert

9

42

transformers

0

token-classification

false

true

false

cc-by-4.0

null

0

['generated_from_keras_callback']

true

2,619

false

# constituency-brackets-20 This model is a fine-tuned version of [allegro/herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2020 - Train Acc: 0.9356 - Validation Loss: 0.2929 - Validation Acc: 0.9120 - Epoch: 19 ## 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': 5e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Acc | Validation Loss | Validation Acc | Epoch | |:----------:|:---------:|:---------------:|:--------------:|:-----:| | 2.4703 | 0.3783 | 1.4719 | 0.5858 | 0 | | 1.2149 | 0.6600 | 0.8922 | 0.7269 | 1 | | 0.8721 | 0.7343 | 0.6914 | 0.7779 | 2 | | 0.7186 | 0.7715 | 0.6028 | 0.8037 | 3 | | 0.6239 | 0.7987 | 0.5427 | 0.8240 | 4 | | 0.5432 | 0.8342 | 0.4469 | 0.8677 | 5 | | 0.4521 | 0.8665 | 0.4092 | 0.8760 | 6 | | 0.4100 | 0.8761 | 0.3867 | 0.8819 | 7 | | 0.3792 | 0.8855 | 0.3761 | 0.8849 | 8 | | 0.3526 | 0.8926 | 0.3469 | 0.8938 | 9 | | 0.3304 | 0.8981 | 0.3433 | 0.8944 | 10 | | 0.3091 | 0.9049 | 0.3329 | 0.8977 | 11 | | 0.2935 | 0.9081 | 0.3178 | 0.9028 | 12 | | 0.2769 | 0.9138 | 0.3140 | 0.9032 | 13 | | 0.2614 | 0.9173 | 0.2994 | 0.9114 | 14 | | 0.2472 | 0.9213 | 0.2954 | 0.9128 | 15 | | 0.2344 | 0.9260 | 0.2899 | 0.9142 | 16 | | 0.2229 | 0.9292 | 0.2971 | 0.9092 | 17 | | 0.2136 | 0.9322 | 0.2872 | 0.9143 | 18 | | 0.2020 | 0.9356 | 0.2929 | 0.9120 | 19 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.8.0 - Tokenizers 0.13.2

f0b922b945695cbf0248efa9c366e8bc

bofenghuang/whisper-medium-cv11-german

bofenghuang

whisper

17

38

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_11_0']

null

0

['automatic-speech-recognition', 'whisper-event']

true

4,483

false

f8b6b0a47375a05365402c0d45255aab

muhtasham/small-mlm-glue-mrpc-custom-tokenizer

muhtasham

bert

12

13

transformers

1

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,411

false

# small-mlm-glue-mrpc-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.4085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.9986 | 1.09 | 500 | 6.7224 | | 6.2058 | 2.18 | 1000 | 6.3947 | | 5.981 | 3.27 | 1500 | 6.4669 | | 5.8487 | 4.36 | 2000 | 6.6145 | | 5.7411 | 5.45 | 2500 | 6.4085 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

a8f0cba2af6906435d57adc4c955318b

chaitu619/chai_librispeech_asr_train_transducer_v2_raw_en_bpe5000_sp

chaitu619

null

31

0

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['librispeech_asr', 'librispeech 960h']

null

0

1

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

57,248

false

## ESPnet2 model This model was trained by Chaitanya Narisetty using recipe in [espnet](https://github.com/espnet/espnet/).  # RESULTS ## Environments - date: `Tue Apr 26 15:33:18 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 202204` - pytorch version: `pytorch 1.8.1+cu111` - Git hash: `8a76ff24eb513d96561fb47d0320dd39c1c3645a` - Commit date: `Tue Apr 19 07:32:58 2022 +0000` ## asr_train_conformer-rnn_transducer_raw_en_bpe5000_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.loss.ave_10best/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.5| |decode_asr_model_valid.loss.ave_10best/dev_other|2864|50948|93.8|5.6|0.6|0.6|6.8|50.8| |decode_asr_model_valid.loss.ave_10best/test_clean|2620|52576|97.5|2.3|0.2|0.3|2.8|32.7| |decode_asr_model_valid.loss.ave_10best/test_other|2939|52343|94.1|5.3|0.6|0.7|6.6|51.8| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_clean|2703|54402|98.0|1.8|0.2|0.2|2.2|28.2| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_other|2864|50948|94.8|4.5|0.7|0.5|5.7|45.1| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.4|29.3| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_other|2939|52343|94.9|4.3|0.7|0.5|5.6|47.0| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.loss.ave_10best/dev_clean|2703|288456|99.4|0.4|0.3|0.2|0.9|31.5| |decode_asr_model_valid.loss.ave_10best/dev_other|2864|265951|97.7|1.4|0.9|0.8|3.0|50.8| |decode_asr_model_valid.loss.ave_10best/test_clean|2620|281530|99.4|0.4|0.3|0.3|0.9|32.7| |decode_asr_model_valid.loss.ave_10best/test_other|2939|272758|97.9|1.2|0.9|0.8|2.8|51.8| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|28.2| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_other|2864|265951|97.9|1.1|1.0|0.6|2.7|45.1| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_clean|2620|281530|99.4|0.3|0.3|0.2|0.9|29.3| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_other|2939|272758|98.1|0.9|1.0|0.6|2.5|47.0| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_model_valid.loss.ave_10best/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.3|31.5| |decode_asr_model_valid.loss.ave_10best/dev_other|2864|63110|92.7|5.6|1.7|1.2|8.6|50.8| |decode_asr_model_valid.loss.ave_10best/test_clean|2620|65818|97.0|2.2|0.9|0.4|3.4|32.7| |decode_asr_model_valid.loss.ave_10best/test_other|2939|65101|93.0|5.1|1.9|1.0|8.0|51.8| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_clean|2703|68010|97.5|1.8|0.8|0.4|2.9|28.2| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_other|2864|63110|93.5|4.5|1.9|0.9|7.4|45.1| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_clean|2620|65818|97.3|1.9|0.8|0.4|3.0|29.3| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_other|2939|65101|93.9|4.1|1.9|0.8|6.9|47.0| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/transducer/train_conformer-rnn_transducer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer-rnn_transducer_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 46179 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 25 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 10000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_960_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_960_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0015 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁THE - S - ▁AND - ▁OF - ▁TO - ▁A - ▁IN - ▁I - ▁HE - ▁THAT - ▁WAS - ED - ▁IT - '''' - ▁HIS - ING - ▁YOU - ▁WITH - ▁FOR - ▁HAD - T - ▁AS - ▁HER - ▁IS - ▁BE - ▁BUT - ▁NOT - ▁SHE - D - ▁AT - ▁ON - LY - ▁HIM - ▁THEY - ▁ALL - ▁HAVE - ▁BY - ▁SO - ▁THIS - ▁MY - ▁WHICH - ▁ME - ▁SAID - ▁FROM - ▁ONE - Y - E - ▁WERE - ▁WE - ▁NO - N - ▁THERE - ▁OR - ER - ▁AN - ▁WHEN - ▁ARE - ▁THEIR - ▁WOULD - ▁IF - ▁WHAT - ▁THEM - ▁WHO - ▁OUT - M - ▁DO - ▁WILL - ▁UP - ▁BEEN - P - R - ▁MAN - ▁THEN - ▁COULD - ▁MORE - C - ▁INTO - ▁NOW - ▁VERY - ▁YOUR - ▁SOME - ▁LITTLE - ES - ▁TIME - RE - ▁CAN - ▁LIKE - LL - ▁ABOUT - ▁HAS - ▁THAN - ▁DID - ▁UPON - ▁OVER - IN - ▁ANY - ▁WELL - ▁ONLY - B - ▁SEE - ▁GOOD - ▁OTHER - ▁TWO - L - ▁KNOW - ▁GO - ▁DOWN - ▁BEFORE - A - AL - ▁OUR - ▁OLD - ▁SHOULD - ▁MADE - ▁AFTER - ▁GREAT - ▁DAY - ▁MUST - ▁COME - ▁HOW - ▁SUCH - ▁CAME - LE - ▁WHERE - ▁US - ▁NEVER - ▁THESE - ▁MUCH - ▁DE - ▁MISTER - ▁WAY - G - ▁S - ▁MAY - ATION - ▁LONG - OR - ▁AM - ▁FIRST - ▁BACK - ▁OWN - ▁RE - ▁AGAIN - ▁SAY - ▁MEN - ▁WENT - ▁HIMSELF - ▁HERE - NESS - ▁THINK - V - IC - ▁EVEN - ▁THOUGHT - ▁HAND - ▁JUST - ▁O - ▁UN - VE - ION - ▁ITS - 'ON' - ▁MAKE - ▁MIGHT - ▁TOO - K - ▁AWAY - ▁LIFE - TH - ▁WITHOUT - ST - ▁THROUGH - ▁MOST - ▁TAKE - ▁DON - ▁EVERY - F - O - ▁SHALL - ▁THOSE - ▁EYES - AR - ▁STILL - ▁LAST - ▁HOUSE - ▁HEAD - ABLE - ▁NOTHING - ▁NIGHT - ITY - ▁LET - ▁MANY - ▁OFF - ▁BEING - ▁FOUND - ▁WHILE - EN - ▁SAW - ▁GET - ▁PEOPLE - ▁FACE - ▁YOUNG - CH - ▁UNDER - ▁ONCE - ▁TELL - AN - ▁THREE - ▁PLACE - ▁ROOM - ▁YET - ▁SAME - IL - US - U - ▁FATHER - ▁RIGHT - EL - ▁THOUGH - ▁ANOTHER - LI - RI - ▁HEART - IT - ▁PUT - ▁TOOK - ▁GIVE - ▁EVER - ▁E - ▁PART - ▁WORK - ERS - ▁LOOK - ▁NEW - ▁KING - ▁MISSUS - ▁SIR - ▁LOVE - ▁MIND - ▁LOOKED - W - RY - ▁ASKED - ▁LEFT - ET - ▁LIGHT - CK - ▁DOOR - ▁MOMENT - RO - ▁WORLD - ▁THINGS - ▁HOME - UL - ▁THING - LA - ▁WHY - ▁MOTHER - ▁ALWAYS - ▁FAR - FUL - ▁WATER - CE - IVE - UR - ▁HEARD - ▁SOMETHING - ▁SEEMED - I - LO - ▁BECAUSE - OL - ▁END - ▁TOLD - ▁CON - ▁YES - ▁GOING - ▁GOT - RA - IR - ▁WOMAN - ▁GOD - EST - TED - ▁FIND - ▁KNEW - ▁SOON - ▁EACH - ▁SIDE - H - TON - MENT - ▁OH - NE - Z - LING - ▁AGAINST - TER - ▁NAME - ▁MISS - ▁QUITE - ▁WANT - ▁YEARS - ▁FEW - ▁BETTER - ENT - ▁HALF - ▁DONE - ▁ALSO - ▁BEGAN - ▁HAVING - ▁ENOUGH - IS - ▁LADY - ▁WHOLE - LESS - ▁BOTH - ▁SEEN - ▁SET - ▁WHITE - ▁COURSE - IES - ▁VOICE - ▁CALLED - ▁D - ▁EX - ATE - ▁TURNED - ▁GAVE - ▁C - ▁POOR - MAN - UT - NA - ▁DEAR - ISH - ▁GIRL - ▁MORNING - ▁BETWEEN - LED - ▁NOR - IA - ▁AMONG - MA - ▁ - ▁SMALL - ▁REST - ▁WHOM - ▁FELT - ▁HANDS - ▁MYSELF - ▁HIGH - ▁M - ▁HOWEVER - ▁HERSELF - ▁P - CO - ▁STOOD - ID - ▁KIND - ▁HUNDRED - AS - ▁ROUND - ▁ALMOST - TY - ▁SINCE - ▁G - AM - ▁LA - SE - ▁BOY - ▁MA - ▁PERHAPS - ▁WORDS - ATED - ▁HO - X - ▁MO - ▁SAT - ▁REPLIED - ▁FOUR - ▁ANYTHING - ▁TILL - ▁UNTIL - ▁BLACK - TION - ▁CRIED - RU - TE - ▁FACT - ▁HELP - ▁NEXT - ▁LOOKING - ▁DOES - ▁FRIEND - ▁LAY - ANCE - ▁POWER - ▁BROUGHT - VER - ▁FIRE - ▁KEEP - PO - FF - ▁COUNTRY - ▁SEA - ▁WORD - ▁CAR - ▁DAYS - ▁TOGETHER - ▁IMP - ▁REASON - KE - ▁INDEED - TING - ▁MATTER - ▁FULL - ▁TEN - TIC - ▁LAND - ▁RATHER - ▁AIR - ▁HOPE - ▁DA - ▁OPEN - ▁FEET - ▁EN - ▁FIVE - ▁POINT - ▁CO - OM - ▁LARGE - ▁B - ▁CL - ME - ▁GONE - ▁CHILD - INE - GG - ▁BEST - ▁DIS - UM - ▁HARD - ▁LORD - OUS - ▁WIFE - ▁SURE - ▁FORM - DE - ▁DEATH - ANT - ▁NATURE - ▁BA - ▁CARE - ▁BELIEVE - PP - ▁NEAR - ▁RO - ▁RED - ▁WAR - IE - ▁SPEAK - ▁FEAR - ▁CASE - ▁TAKEN - ▁ALONG - ▁CANNOT - ▁HEAR - ▁THEMSELVES - CI - ▁PRESENT - AD - ▁MASTER - ▁SON - ▁THUS - ▁LI - ▁LESS - ▁SUN - ▁TRUE - IM - IOUS - ▁THOUSAND - ▁MONEY - ▁W - ▁BEHIND - ▁CHILDREN - ▁DOCTOR - AC - ▁TWENTY - ▁WISH - ▁SOUND - ▁WHOSE - ▁LEAVE - ▁ANSWERED - ▁THOU - ▁DUR - ▁HA - ▁CERTAIN - ▁PO - ▁PASSED - GE - TO - ▁ARM - ▁LO - ▁STATE - ▁ALONE - TA - ▁SHOW - ▁NEED - ▁LIVE - ND - ▁DEAD - ENCE - ▁STRONG - ▁PRE - ▁TI - ▁GROUND - SH - TI - ▁SHORT - IAN - UN - ▁PRO - ▁HORSE - MI - ▁PRINCE - ARD - ▁FELL - ▁ORDER - ▁CALL - AT - ▁GIVEN - ▁DARK - ▁THEREFORE - ▁CLOSE - ▁BODY - ▁OTHERS - ▁SENT - ▁SECOND - ▁OFTEN - ▁CA - ▁MANNER - MO - NI - ▁BRING - ▁QUESTION - ▁HOUR - ▁BO - AGE - ▁ST - ▁TURN - ▁TABLE - ▁GENERAL - ▁EARTH - ▁BED - ▁REALLY - ▁SIX - 'NO' - IST - ▁BECOME - ▁USE - ▁READ - ▁SE - ▁VI - ▁COMING - ▁EVERYTHING - ▁EM - ▁ABOVE - ▁EVENING - ▁BEAUTIFUL - ▁FEEL - ▁RAN - ▁LEAST - ▁LAW - ▁ALREADY - ▁MEAN - ▁ROSE - WARD - ▁ITSELF - ▁SOUL - ▁SUDDENLY - ▁AROUND - RED - ▁ANSWER - ICAL - ▁RA - ▁WIND - ▁FINE - ▁WON - ▁WHETHER - ▁KNOWN - BER - NG - ▁TA - ▁CAPTAIN - ▁EYE - ▁PERSON - ▁WOMEN - ▁SORT - ▁ASK - ▁BROTHER - ▁USED - ▁HELD - ▁BIG - ▁RETURNED - ▁STRANGE - ▁BU - ▁PER - ▁FREE - ▁EITHER - ▁WITHIN - ▁DOUBT - ▁YEAR - ▁CLEAR - ▁SIGHT - ▁GRA - ▁LOST - ▁KEPT - ▁F - PE - ▁BAR - ▁TOWN - ▁SLEEP - ARY - ▁HAIR - ▁FRIENDS - ▁DREAM - ▁FELLOW - PER - ▁DEEP - QUE - ▁BECAME - ▁REAL - ▁PAST - ▁MAKING - RING - ▁COMP - ▁ACT - ▁BAD - HO - STER - ▁YE - ▁MEANS - ▁RUN - MEN - ▁DAUGHTER - ▁SENSE - ▁CITY - ▁SOMETIMES - ▁TOWARDS - ▁ROAD - ▁SP - ▁LU - ▁READY - ▁FOOT - ▁COLD - ▁SA - ▁LETTER - ▁ELSE - ▁MAR - ▁STA - BE - ▁TRUTH - ▁LE - BO - ▁BUSINESS - CHE - ▁JOHN - ▁SUBJECT - ▁COURT - ▁IDEA - ILY - ▁RIVER - ATING - ▁FAMILY - HE - ▁DIDN - ▁GLAD - ▁SEVERAL - IAL - ▁UNDERSTAND - ▁SC - ▁POSSIBLE - ▁DIFFERENT - ▁RETURN - ▁ARMS - ▁LOW - ▁HOLD - ▁TALK - ▁RU - ▁WINDOW - ▁INTEREST - ▁SISTER - SON - ▁SH - ▁BLOOD - ▁SAYS - ▁CAP - ▁DI - ▁HUMAN - ▁CAUSE - NCE - ▁THANK - ▁LATE - GO - ▁CUT - ▁ACROSS - ▁STORY - NT - ▁COUNT - ▁ABLE - DY - LEY - ▁NUMBER - ▁STAND - ▁CHURCH - ▁THY - ▁SUPPOSE - LES - BLE - OP - ▁EFFECT - BY - ▁K - ▁NA - ▁SPOKE - ▁MET - ▁GREEN - ▁HUSBAND - ▁RESPECT - ▁PA - ▁FOLLOWED - ▁REMEMBER - ▁LONGER - ▁AGE - ▁TAKING - ▁LINE - ▁SEEM - ▁HAPPY - LAND - EM - ▁STAY - ▁PLAY - ▁COMMON - ▁GA - ▁BOOK - ▁TIMES - ▁OBJECT - ▁SEVEN - QUI - DO - UND - ▁FL - ▁PRETTY - ▁FAIR - WAY - ▁WOOD - ▁REACHED - ▁APPEARED - ▁SWEET - ▁FALL - BA - ▁PASS - ▁SIGN - ▁TREE - IONS - ▁GARDEN - ▁ILL - ▁ART - ▁REMAIN - ▁OPENED - ▁BRIGHT - ▁STREET - ▁TROUBLE - ▁PAIN - ▁CONTINUED - ▁SCHOOL - OUR - ▁CARRIED - ▁SAYING - HA - ▁CHANGE - ▁FOLLOW - ▁GOLD - ▁SW - ▁FEELING - ▁COMMAND - ▁BEAR - ▁CERTAINLY - ▁BLUE - ▁NE - CA - ▁WILD - ▁ACCOUNT - ▁OUGHT - UD - ▁T - ▁BREATH - ▁WANTED - ▁RI - ▁HEAVEN - ▁PURPOSE - ▁CHARACTER - ▁RICH - ▁PE - ▁DRESS - OS - FA - ▁TH - ▁ENGLISH - ▁CHANCE - ▁SHIP - ▁VIEW - ▁TOWARD - AK - ▁JOY - ▁JA - ▁HAR - ▁NEITHER - ▁FORCE - ▁UNCLE - DER - ▁PLAN - ▁PRINCESS - DI - ▁CHIEF - ▁HAT - ▁LIVED - ▁AB - ▁VISIT - ▁MOR - TEN - ▁WALL - UC - ▁MINE - ▁PLEASURE - ▁SMILE - ▁FRONT - ▁HU - ▁DEAL - OW - ▁FURTHER - GED - ▁TRIED - DA - VA - ▁NONE - ▁ENTERED - ▁QUEEN - ▁PAY - ▁EL - ▁EXCEPT - ▁SHA - ▁FORWARD - ▁EIGHT - ▁ADDED - ▁PUBLIC - ▁EIGHTEEN - ▁STAR - ▁HAPPENED - ▁LED - ▁WALKED - ▁ALTHOUGH - ▁LATER - ▁SPIRIT - ▁WALK - ▁BIT - ▁MEET - LIN - ▁FI - LT - ▁MOUTH - ▁WAIT - ▁HOURS - ▁LIVING - ▁YOURSELF - ▁FAST - ▁CHA - ▁HALL - ▁BEYOND - ▁BOAT - ▁SECRET - ENS - ▁CHAIR - RN - ▁RECEIVED - ▁CAT - RESS - ▁DESIRE - ▁GENTLEMAN - UGH - ▁LAID - EVER - ▁OCCASION - ▁WONDER - ▁GU - ▁PARTY - DEN - ▁FISH - ▁SEND - ▁NEARLY - ▁TRY - CON - ▁SEEMS - RS - ▁BELL - ▁BRA - ▁SILENCE - IG - ▁GUARD - ▁DIE - ▁DOING - ▁TU - ▁COR - ▁EARLY - ▁BANK - ▁FIGURE - IF - ▁ENGLAND - ▁MARY - ▁AFRAID - LER - ▁FO - ▁WATCH - ▁FA - ▁VA - ▁GRE - ▁AUNT - PED - ▁SERVICE - ▁JE - ▁PEN - ▁MINUTES - ▁PAN - ▁TREES - NED - ▁GLASS - ▁TONE - ▁PLEASE - ▁FORTH - ▁CROSS - ▁EXCLAIMED - ▁DREW - ▁EAT - ▁AH - ▁GRAVE - ▁CUR - PA - URE - CENT - ▁MILES - ▁SOFT - ▁AGO - ▁POSITION - ▁WARM - ▁LENGTH - ▁NECESSARY - ▁THINKING - ▁PICTURE - ▁PI - SHIP - IBLE - ▁HEAVY - ▁ATTENTION - ▁DOG - ABLY - ▁STANDING - ▁NATURAL - ▁APPEAR - OV - ▁CAUGHT - VO - ISM - ▁SPRING - ▁EXPERIENCE - ▁PAT - OT - ▁STOPPED - ▁REGARD - ▁HARDLY - ▁SELF - ▁STRENGTH - ▁GREW - ▁KNIGHT - ▁OPINION - ▁WIDE - ▁INSTEAD - ▁SOUTH - ▁TRANS - ▁CORNER - ▁LEARN - ▁ISLAND - ▁MI - ▁THIRD - ▁STE - ▁STRAIGHT - ▁TEA - ▁BOUND - ▁SEEING - ▁JU - ▁DINNER - ▁BEAUTY - ▁PEACE - AH - ▁REP - ▁SILENT - ▁CRE - ALLY - RIC - ▁STEP - ▁VER - ▁JO - GER - ▁SITTING - ▁THIRTY - ▁SAVE - ENED - ▁GLANCE - ▁REACH - ▁ACTION - ▁SAL - ▁SAD - ▁STONE - ITIES - ▁FRENCH - ▁STRUCK - ▁PAPER - ▁WHATEVER - ▁SUB - ▁DISTANCE - ▁WRONG - ▁KNOWLEDGE - ▁SAFE - ▁SNOW - ▁MUSIC - ▁FIFTY - RON - ▁ATTEMPT - ▁GOVERNMENT - TU - ▁CROWD - ▁BESIDES - ▁LOVED - ▁BOX - ▁DIRECTION - ▁TRAIN - ▁NORTH - ▁THICK - ▁GETTING - AV - ▁FLOOR - ▁COMPANY - ▁BLOW - ▁PLAIN - TRO - ▁BESIDE - ▁ROCK - ▁IMMEDIATELY - FI - ▁SHADOW - ▁SIT - ORS - ILE - ▁DRINK - ▁SPOT - ▁DANGER - ▁AL - ▁SAINT - ▁SLOWLY - ▁PALACE - IER - ▁RESULT - ▁PETER - ▁FOREST - ▁BELONG - ▁SU - ▁PAR - RIS - ▁TEARS - ▁APPEARANCE - ▁GATE - BU - ITION - ▁QUICKLY - ▁QUIET - ▁LONDON - ▁START - ▁BROWN - TRA - KIN - ▁CONSIDER - ▁BATTLE - ▁ANNE - ▁PIECE - ▁DIED - ▁SUCCESS - ▁LIPS - ▁FILLED - ▁FORGET - ▁POST - IFIED - ▁MARGARET - ▁FOOD - HAM - ▁PLEASANT - ▁FE - ▁EXPRESSION - ▁POCKET - ▁FRESH - ▁WEAR - TRI - ▁BROKEN - ▁LAUGHED - GING - ▁FOLLOWING - WN - IP - ▁TOUCH - ▁YOUTH - ATIVE - ▁LEG - ▁WEEK - ▁REMAINED - ▁EASY - NER - RK - ▁ENTER - ▁FIGHT - ▁PLACED - ▁TRAVEL - ▁SIMPLE - ▁GIRLS - ▁WAITING - ▁STOP - ▁WAVE - AU - ▁WISE - ▁CAMP - TURE - UB - ▁VE - ▁OFFICE - ▁GRAND - ▁FIT - ▁JUDGE - UP - MENTS - ▁QUICK - HI - ▁FLO - RIES - VAL - ▁COMFORT - ▁PARTICULAR - ▁STARTED - ▁SUIT - ▁NI - ▁PALE - ▁IMPOSSIBLE - ▁HOT - ▁CONVERSATION - ▁SCENE - ▁BOYS - ▁WIN - ▁BRE - ▁SOCIETY - ▁OUTSIDE - ▁WRITE - ▁EFFORT - ▁TALKING - ▁FORTUNE - ▁NINE - ▁WA - ▁SINGLE - ▁RULE - ▁PORT - ▁WINTER - ▁CAST - ▁CRA - ▁HAPPEN - ▁CRO - ▁SHUT - NING - ▁GUN - ▁NOBLE - ▁BEGIN - ▁PATH - ▁SKY - ▁WONDERFUL - ▁SUDDEN - ▁ARMY - ▁CHE - ▁WORTH - ▁MOUNTAIN - ▁MIN - AG - ▁FLU - ▁GRACE - ▁CHAPTER - ▁BELOW - ▁RING - ▁TURNING - ▁IRON - ▁TOP - ▁AFTERNOON - ORY - ▁EVIL - ▁TRUST - ▁BOW - ▁TRI - ▁SAIL - ▁CONTENT - ▁HORSES - ITE - ▁SILVER - AP - ▁LAD - ▁RUNNING - ▁HILL - ▁BEGINNING - ▁MAD - ▁HABIT - GRA - ▁CLOTHES - ▁MORROW - ▁CRY - ▁FASHION - ▁PRESENCE - ▁Z - FE - ▁ARRIVED - ▁QUARTER - ▁PERFECT - ▁WO - ▁TRA - ▁USUAL - ▁NECK - ▁MARRIED - ▁SEAT - ▁WI - ▁GAR - ▁SAND - ▁SHORE - ▁GIVING - NY - ▁PROBABLY - ▁MINUTE - ▁EXPECT - ▁DU - ▁SHOT - ▁INSTANT - ▁DEGREE - ▁COLOR - ▁WEST - RT - ▁MARCH - ▁BIRD - ▁SHOWED - ▁GREATER - ▁SERIOUS - ▁CARRY - ▁COVERED - ▁FORMER - ▁LOUD - ▁MOVED - ▁MASS - ▁SEEK - ▁CHO - GEN - ▁ROMAN - IB - ▁MOON - ▁BOARD - ▁STREAM - ▁EASILY - ▁WISHED - ▁SEARCH - ▁COULDN - ▁MONTHS - ▁SICK - LIE - ▁DUTY - ▁TWELVE - ▁FAINT - ▁STRANGER - ▁SURPRISE - ▁KILL - ▁LEAVING - ▁JOURNEY - ▁SCARCELY - ▁RAISED - ▁SPEAKING - ▁TERRIBLE - ▁TOM - ▁FIELD - ▁GAME - ▁QUA - ▁PROMISE - ▁LIE - ▁CONDITION - ▁TRO - ▁PERSONAL - ▁TALL - ▁STICK - ▁THREW - ▁MARRY - ▁VAN - ▁BURN - ▁ACCORDING - ▁RISE - ▁ATTACK - ▁SWORD - ▁GUESS - ▁THOUGHTS - ▁THIN - ▁THROW - ▁CALM - SIDE - ▁VILLAGE - ▁DEN - ▁ANXIOUS - ▁MER - GI - ▁EXPECTED - ▁BALL - ▁ESPECIALLY - ▁CHARGE - ▁MEASURE - ISE - ▁NICE - ▁TRYING - ▁ALLOW - ▁SHARP - ▁BREAD - ▁HONOUR - ▁HONOR - ▁ENTIRELY - ▁BILL - ▁BRI - ▁WRITTEN - ▁AR - ▁BROKE - ▁KILLED - ▁MARK - ▁VEN - ▁LADIES - ▁LEARNED - ▁FLOWERS - PLE - ▁FORTY - ▁OFFER - ▁HAPPINESS - ▁PRAY - ▁CLASS - ▁FER - ▁PRINCIPLE - GU - ▁BOOKS - ▁SHAPE - ▁SUMMER - ▁JACK - ▁DRAW - ▁GOLDEN - ▁DECIDED - ▁LEAD - ▁UNLESS - ▁HARM - ▁LISTEN - HER - ▁SHOOK - ▁INFLUENCE - ▁PERFECTLY - ▁MARRIAGE - ▁BROAD - ▁ESCAPE - ▁STATES - ▁MIDDLE - ▁PLANT - ▁MIL - ▁MOVEMENT - ▁NOISE - ▁ENEMY - ▁HISTORY - ▁BREAK - ROUS - ▁UNDERSTOOD - ▁LATTER - FER - ▁COMES - ▁MERELY - ▁SIMPLY - WI - ▁IMAGINE - ▁LOWER - ▁CONDUCT - ▁BORN - WA - ▁YARD - ▁KA - ▁CLOSED - ▁NOTE - GA - ▁STRA - RAN - ▁EXIST - EV - ▁SPEECH - ▁BITTER - JO - ▁MAKES - ▁GRASS - ▁REPLY - ▁CHANGED - ▁MON - ▁LYING - ▁DANCE - ▁FINALLY - ▁AMERICAN - ▁ENJOY - ▁CONTAIN - ▁MEANT - USE - ▁OBSERVED - THER - ▁LAUGH - ▁AFTERWARDS - ▁BEAT - ▁RACE - ▁EQUAL - ▁RAIN - PS - ▁STEPS - ▁BENEATH - ▁TAIL - ▁TASTE - IO - EY - ▁CHAR - ▁GE - GN - TIN - ▁GROW - ▁TE - IANS - ▁MOVE - ▁REPEATED - ▁DRIVE - TUR - ▁SI - CLOCK - ▁BRAVE - ▁MADAME - ▁LOT - ▁CASTLE - ▁HI - AND - ▁FUTURE - ▁RELATION - ▁SORRY - ▁HEALTH - ▁DICK - ▁R - ▁BUILDING - ▁EDGE - ▁BLESS - ▁SPITE - WE - ▁MIS - ▁PRISONER - ▁ALLOWED - ▁PH - ▁CATCH - MER - ETH - ▁COAT - ▁COMPLETE - ▁WOULDN - ▁CREATURE - ▁YELLOW - ▁IMPORTANT - ▁ADD - ▁PASSING - ▁DARKNESS - ▁CARRIAGE - ▁MILL - ▁FIFTEEN - NCY - ▁HUNG - ▁OB - ▁PLEASED - ▁SPREAD - ▁CURIOUS - ▁WORSE - ▁CIRCUMSTANCES - ▁GI - LAR - ▁CAL - ▁HY - ▁MERE - ▁JANE - ▁EAST - BI - ▁CUP - ▁BLIND - ▁PASSION - ▁DISCOVERED - ▁NOTICE - ▁REPORT - ▁SPACE - ▁PRESENTLY - ▁SORROW - ▁PACK - ▁DIN - CY - ▁DRY - ▁ANCIENT - ▁DRESSED - ▁COVER - ▁VO - ▁EXISTENCE - ▁EXACTLY - ▁BEAST - ▁PROPER - ▁DROPPED - ▁CLEAN - ▁COLOUR - ▁HOST - ▁CHAMBER - ▁FAITH - LET - ▁DETERMINED - ▁PRIEST - ▁STORM - ▁SKIN - ▁DARE - ▁PERSONS - ▁PICK - ▁NARROW - ▁SUPPORT - ▁PRIVATE - ▁SMILED - ▁COUSIN - ▁DRAWING - ▁ATTEND - ▁COOK - ▁PREVENT - ▁VARIOUS - ▁BLA - ▁FIXED - ▁WEAK - THE - ▁HOLE - ▁BOTTOM - ▁NOBODY - ADE - ▁LEGS - ITCH - ▁INDIVIDUAL - ▁EARS - LIKE - ▁ADVANTAGE - ▁FRANCE - ▁BON - ▁WINE - ▁LIVES - OD - ▁WALLS - ▁TIRED - ▁SHOP - ▁ANIMAL - ▁CRU - ▁WROTE - ▁ROYAL - ▁CONSIDERED - ▁MORAL - ▁COMPANION - ▁LOSE - ▁ISN - ▁BAG - ▁LAKE - ▁INTER - ▁COM - ▁LETTERS - ▁LUCK - ▁EAR - ▁GERMAN - ▁PET - ▁SAKE - ▁DROP - ▁PAID - ▁BREAKFAST - ▁LABOR - ▁DESERT - ▁DECLARED - ▁HUM - ▁STUDY - ▁INSTANCE - ONE - ▁SOMEWHAT - ▁CLOTH - ▁SPECIAL - ▁COLONEL - ▁SONG - ▁MAIN - ▁VALUE - ▁PROUD - ▁EXPRESS - ▁NATION - ▁HANDSOME - ▁CONFESS - ▁PU - ▁PASSAGE - ▁PERIOD - ▁CUSTOM - ▁HURT - ▁SHOULDER - ▁CHRIST - ZA - ▁RECEIVE - ▁DIFFICULT - ▁DEPEND - ▁MEETING - ▁CHI - ▁GEN - LIGHT - ▁BELIEVED - ▁SOCIAL - ▁DIFFICULTY - ▁GREATEST - ▁DRAWN - ▁GRANT - ▁BIRDS - ▁ANGRY - ▁HEAT - UFF - ▁DUE - ▁PLACES - ▁SIN - ▁COURAGE - ▁EVIDENTLY - ▁GENTLE - ▁CRUEL - ▁GEORGE - ▁GRI - ▁SERVANT - ▁U - ▁PURE - OOK - 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IZING - ▁GUILTY - ▁VICTOR - WORTH - ▁PRA - ▁APPROACHING - ▁RELATIVE - ▁ESTATE - ▁UGLY - ▁METAL - ▁ROBERT - ▁TENT - ▁ADMIRATION - ▁FOURTEEN - ▁BARBAR - ▁WITCH - ELLA - ▁CAKE - ▁SHONE - ▁MANAGED - ▁VOLUME - ▁GREEK - ▁DANCING - ▁WRETCHED - ▁CONDEMN - ▁MAGNIFICENT - ▁CONSULT - J - ▁ORGAN - ▁FLEET - ▁ARRANGEMENT - ▁INCIDENT - ▁MISERY - ▁ARROW - ▁STROKE - ▁ASSIST - ▁BUILD - ▁SUCCEED - ▁DESPERATE - ▁WIDOW - UDE - ▁MARKET - ▁WISDOM - ▁PRECISE - ▁CURRENT - ▁SPOIL - ▁BADE - ▁WOODEN - ▁RESIST - ▁OBVIOUS - ▁SENSIBLE - FALL - ▁ADDRESSED - ▁GIL - ▁COUNSEL - ▁PURCHASE - ▁SELECT - ▁USELESS - ▁STARED - ▁ARREST - ▁POISON - ▁FIN - ▁SWALLOW - ▁BLOCK - ▁SLID - ▁NINETY - ▁SPORT - ▁PROVIDE - ▁ANNA - ▁LAMB - ▁INTERVAL - ▁JUMP - ▁DESCRIBED - ▁STRIKING - ▁PROVISION - ▁PROPOSED - ▁MELANCHOLY - ▁WARRIOR - ▁SUGGEST - ▁DEPARTURE - ▁BURDEN - ▁LIMB - ▁TROUBLED - ▁MEADOW - ▁SACRED - ▁SOLID - ▁TRU - ▁LUCY - ▁RECOVER - ▁ENERGY - ▁POWDER - ▁RESUMED - ▁INTENSE - ▁BRITISH - ▁STRAW - ▁AGREEABLE - ▁EVERYONE - ▁CONCERN - ▁VOYAGE - 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▁COMMENT - ▁APPROVE - ▁CEREMONY - ▁CRIMINAL - ▁SCIENTIFIC - ▁DUCHESS - ▁VIVID - ▁SHIFT - ▁AVAIL - ▁DAMP - ▁JOHNSON - ▁SLENDER - ▁CONTRAST - ▁AMUSEMENT - ▁PLOT - ▁LYN - ▁ASSOCIATION - ▁SNATCH - ▁UNCERTAIN - ▁PRESSURE - ▁PERCH - ▁APPLY - ▁PLANET - ▁NOTWITHSTANDING - ▁SWUNG - ▁STIRRED - ▁ATTENDANT - ▁ENJOYMENT - ▁WORRY - ▁ALBERT - ▁NAKED - ▁TALENT - ▁MARIAN - ▁REFORM - ▁DELIBERATE - ▁INTELLIGENT - ▁SENSITIVE - ▁YONDER - ▁PUPIL - ▁FRIGHTFUL - ▁DOUBTFUL - ▁STANDARD - ▁MAGISTRATE - ▁SHEPHERD - ▁STOMACH - ▁DEPOSIT - ▁RENEW - ▁HEDGE - ▁FRANCS - ▁POSSIBILITY - ▁RESEMBLE - ▁FATIGUE - ▁PORTRAIT - ▁FAVORITE - ▁CREAM - ▁BURG - ▁SECRETARY - ▁DIVERS - ▁ACTIVITY - ▁SPECULAT - ▁HUMOUR - ▁FITTED - ▁EXTERNAL - ▁CETERA - ▁WRAPPED - ▁WHIT - ▁FRED - ▁EXAMINATION - ▁LODGING - ▁OWING - ▁JAW - ▁CROW - ▁BALANCE - ▁PUFF - ▁TENDERNESS - ▁PORTHOS - ▁ANCHOR - ▁INTERRUPT - ▁NECESSARILY - ▁PERPETUAL - ▁AGONY - ▁POPE - ▁SCHOLAR - ▁SCOTLAND - ▁SUPPRESS - ▁WRATH - ▁WRECK - ▁EXCEED - ▁PERFECTION - ▁INDIA - ▁TRADITION - ▁SECTION - ▁EASTERN - ▁DOORWAY - ▁WIVES - ▁CONVENTION - ▁ANNOUNC - ▁EGYPT - ▁CONTRADICT - ▁SCRATCH - ▁CENTRAL - ▁GLOVE - ▁WAX - ▁PREPARE - ▁ACCOMPANY - ▁INCREASING - ▁LIBERAL - ▁RAISING - ▁ORANGE - ▁SHOE - ▁ATTRIBUTE - ▁LITERATURE - ▁PUZZLED - ▁WITHDRAW - ▁WHITHER - ▁HAWK - ▁MOONLIGHT - ▁EXAMINE - ▁HAPPILY - ▁PRECEDE - ▁DETECTIVE - ▁INCHES - ▁SOLITARY - ▁DUTCH - ▁NAPOLEON - ▁UNEASY - ▁CARDINAL - ▁BLEW - ▁FOWL - ▁DECORAT - ▁CHILDHOOD - ▁TORMENT - ▁LOSING - ▁PERMISSION - ▁BLANK - ▁UPSTAIRS - ▁CAPACITY - ▁TRIFLE - ▁FOLLY - ▁RECOGNIZE - ▁REMOVE - ▁VENGEANCE - ▁ENTERPRISE - ▁BEDROOM - ▁ANYHOW - ▁INQUIRY - ▁ASHES - ▁DRAG - ▁HUSH - ▁AWKWARD - ▁SATURDAY - ▁GENUINE - ▁SURVIV - ▁SKIRT - ▁AFFECTIONATE - ▁TANG - ▁MUTUAL - ▁DISPUTE - ▁EAGLE - ▁INCOME - ▁BIND - ▁FAME - ▁IMPROVEMENT - ROVING - ▁DIFFER - ▁AWOKE - ▁SLEEVE - ▁SOLITUDE - ▁FAVOURITE - JI - ▁DETECT - ▁COMPREHEND - ▁PREPARING - ▁SERPENT - ▁SUMMIT - ▁KNOT - ▁KNIT - ▁COPY - ▁STOPPING - ▁FADED - ▁HIDEOUS - ▁JULIE - STEAD - ▁SHINE - ▁CONFLICT - ▁PROPOSITION - ▁REFUGE - ▁GALLERY - ▁BUNDLE - ▁AXE - ▁SLAVERY - ▁MASK - ▁ALYOSHA - ▁LADDER - ▁DEPARTMENT - ▁DISCHARGE - ▁DEPRESS - ▁GALLOP - ▁SCARLET - ▁KITTY - ▁RECEIVING - ▁SURRENDER - ▁SUSTAIN - ▁TWILIGHT - ▁CONGRESS - ▁IRELAND - ▁FUNNY - ▁LEND - ▁CONSTITUTE - ▁FUNERAL - ▁CRYSTAL - ▁SPAIN - ▁EXCEEDINGLY - ▁DAMN - ▁COMMUN - ▁CIVILIZATION - ▁PREJUDICE - ▁PORCH - ▁ASSISTANT - ▁INDUSTRY - ▁TUMBLE - ▁DEFENCE - ▁HITHER - ▁SMOT - ▁COLONI - ▁AMAZEMENT - ▁MARGUERITE - ▁MIRACLE - ▁INHERIT - ▁BEGGAR - ▁ENVELOPE - ▁INDIGNATION - ▁NATASHA - ▁PROPOSAL - ▁FRAGMENT - ▁ROUSED - ▁ROAST - ENCIES - ▁COMMENCED - ▁RESOURCE - ▁POPULATION - ▁QUOTH - ▁PURSUE - ▁EDUCAT - ▁AFFLICT - ▁CONTACT - ▁CRIMSON - ▁DIVISION - ▁DISORDER - ▁COPPER - ▁SOLICIT - ▁MODERATE - ▁DRUM - ▁SWIM - ▁SALUTE - ▁ASSUME - ▁MUSCLE - ▁OVERWHELM - ▁SHAKESPEARE - ▁STRUGGLING - ▁TRANQUIL - ▁CHICKEN - ▁TREAD - ▁CLAW - ▁BIBLE - ▁RIDGE - ▁THREAT - ▁VELVET - ▁EXPOSED - ▁IDIOT - ▁BARREL - ▁PENNY - ▁TEMPTATION - ▁DANGLARS - ▁CENTURIES - ▁DISTRIBUT - ▁REJECT - ▁RETORTED - ▁CONCENTRAT - ▁CORDIAL - ▁MOTOR - ▁CANNON - KEEP - ▁WRETCH - ▁ASSURANCE - ▁THIEF - ▁SURVEY - ▁VITAL - ▁RAILWAY - ▁JACKSON - ▁CRASH - ▁GROWL - ▁COMBAT - ▁RECOLLECTION - ▁SECURITY - ▁JACOB - ▁CLUTCH - ▁BLANKET - ▁NANCY - ▁CELLAR - ▁CONVENIENT - ▁INDIGNANT - ▁COARSE - ▁WORM - ▁SCREEN - ▁TRANSPORT - ▁BULLET - ▁APPRECIATE - ▁DEVOTION - ▁INVISIBLE - ▁DRIED - ▁MIXTURE - ▁CANDID - ▁PERFORMANCE - ▁RIPE - ▁EXQUISITE - ▁BARGAIN - ▁TOBACCO - ▁LOYAL - ▁MOULD - ▁ATTENTIVE - ▁DOROTHY - ▁BRUTE - ▁ESTABLISHMENT - ▁ABILITY - ▁INHABIT - ▁OBSCURE - ▁BORROW - ▁ESSENCE - ▁DISMAY - ▁FLEE - ▁BLADE - ▁PLUCK - ▁COFFIN - ▁SUNSET - ▁STEPHEN - ▁ECONOMIC - ▁HOLIDAY - ▁MECHANICAL - ▁COTTON - ▁AWAKENED - ▁SEIZE - ▁RIDICULOUS - ▁SANCHO - ▁HESITATION - ▁CORPSE - ▁SAVING - HOLD - FOOT - ▁ELDEST - ▁DESPITE - ▁EDITH - ▁CHERISH - ▁RESISTANCE - ▁WILSON - ▁ARGUE - ▁INQUIRE - ▁APPREHENSION - ▁AVENUE - ▁DRAKE - ▁PROPOSE - HURST - ▁INFERIOR - ▁STAIRCASE - ▁WHEREFORE - ▁CARLYLE - ▁COUCH - ▁ROUTE - ▁POLITICS - ▁TOMORROW - ▁THRONG - ▁NAUGHT - ▁SUNLIGHT - ▁INDIFFERENCE - ▁OBEDIENCE - ▁RECEPTION - ▁VEGETABLE - ▁IMPERFECT - ▁RESIDENCE - ▁TURKEY - ▁VIOLET - ▁SARAH - ▁ALTAR - ▁GRIEVE - ▁JERK - ▁ENSU - ▁MAGICIAN - ▁BLOSSOM - ▁LANTERN - ▁RESOLUTE - ▁THOUGHTFULLY - ▁FORTNIGHT - ▁TRUMPET - ▁VALJEAN - ▁UNWILLING - ▁LECTURE - ▁WHEREUPON - ▁HOLLAND - ▁CHANGING - ▁CREEK - ▁SLICE - ▁NORMAL - ▁ANNIE - ▁ACCENT - ▁FREDERICK - ▁DISAGREEABLE - ▁RUBBED - ▁DUMB - ▁ESTABLISH - ▁IMPORT - ▁AFFIRM - ▁MATTHEW - ▁BRISK - ▁CONVERT - ▁BENDING - ▁IVAN - ▁MADEMOISELLE - ▁MICHAEL - ▁EASIER - ▁JONES - ▁FACING - ▁EXCELLENCY - ▁LITERARY - ▁GOSSIP - ▁DEVOUR - ▁STAGGER - ▁PENCIL - ▁AVERAGE - ▁HAMMER - ▁TRIUMPHANT - ▁PREFERRED - ▁APPLICATION - ▁OCCUPY - ▁AUTHORITIES - BURN - ▁ASCERTAIN - ▁CORRIDOR - ▁DELICIOUS - ▁PRACTISE - ▁UNIVERSE - ▁SHILLING - ▁CONTEST - ▁ASHORE - ▁COMMIT - ▁ADMINISTRATION - ▁STUDIED - ▁RIGID - ▁ADORN - ▁ELSEWHERE - ▁INNOCENCE - ▁JOURNAL - ▁LANDSCAPE - ▁TELEGRAPH - ▁ANGRILY - ▁CAMPAIGN - ▁UNJUST - ▁CHALLENGE - ▁TORRENT - ▁RELATE - ▁ASSEMBLED - ▁IMPRESSED - ▁CANOE - ▁CONCLUD - ▁QUIXOTE - ▁SATISFACTORY - ▁NIECE - ▁DEAF - ▁RAFT - ▁JIMMY - ▁GLID - ▁REGULAT - ▁CHATTER - ▁GLACIER - ▁ENVY - ▁STATUE - ▁BOSTON - ▁RICHMOND - ▁DENIED - ▁FANNY - ▁SOLOMON - ▁VULGAR - ▁STALK - ▁REPLACE - ▁SPOON - ▁BASIN - ▁FEATURE - ▁CONVICT - ▁ARCHITECT - ▁ADMIRAL - ▁RIBBON - ▁PERMANENT - ▁APRIL - ▁JOLLY - ▁NEIGHBORHOOD - ▁IMPART - BOROUGH - CAMP - ▁HORRID - ▁IMMORTAL - ▁PRUDENCE - ▁SPANIARD - ▁SUPPOSING - ▁TELEPHONE - ▁TEMPERATURE - ▁PENETRATE - ▁OYSTER - ▁APPOINTMENT - ▁EGYPTIAN - ▁DWELT - ▁NEPHEW - ▁RAILROAD - ▁SEPTEMBER - ▁DEVICE - ▁WHEAT - ▁GILBERT - ▁ELEGANT - ▁ADVERTISE - ▁RATIONAL - ▁TURTLE - ▁BROOD - ▁ASSEMBLY - ▁CULTIVATE - ▁EDITOR - ▁SPECIMEN - ▁UNDOUBTEDLY - ▁WHALE - ▁DROPPING - ▁BALLOON - ▁MEDICAL - COMB - ▁COMPOSITION - ▁FOOTSTEPS - ▁LAUNCELOT - ▁DISCOURSE - ▁ERRAND - ▁CONVERSE - ▁ADVANCING - ▁DOWNSTAIRS - ▁TUMULT - ▁CORRUPT - ▁SUFFICE - ▁ANGUISH - ▁SHAGGY - ▁RETIRE - ▁TIMBER - ▁BLAZE - ▁ABSTRACT - ▁EMBROIDER - ▁PHOTOGRAPH - ▁PROSPERITY - ▁TERRIBLY - ▁TERRITORY - ▁THRESHOLD - ▁PAVEMENT - ▁INJURED - ▁LIMP - ▁AGITATION - ▁RASCAL - ▁PRESUME - ▁OBSERVING - ▁OBSTACLE - ▁SIMPLICITY - ▁SLUMBER - ▁SUPPLIED - ▁COMBINATION - ▁DRAIN - ▁WILDERNESS - ▁BELIEVING - ▁VILLAIN - ▁RECKLESS - ▁INJURY - ▁CLAPP - ▁FRIDAY - ▁HERCULES - ▁KENNEDY - ▁SYMPTOM - ▁SLEDGE - ▁CEILING - ▁LEMON - ▁PLAGUE - ▁MONDAY - ▁CANVAS - ▁IMPATIENCE - ▁UNCOMFORTABLE - ▁ACCESS - ▁FROZEN - ▁SENATOR - ▁FRANZ - ▁SWIMMING - ▁BARRIER - ▁ADJUST - ▁COMPARISON - ▁PROCLAIM - ▁WRINKL - ▁OVERLOOK - ▁MITYA - ▁GUILT - ▁PERCEPTION - ▁PRECAUTION - ▁SPECTATOR - ▁SURPRISING - ▁DISTRACT - ▁DISDAIN - ▁BONNET - ▁MAGNET - ▁PROFESS - ▁CONFOUND - ▁NARRATIVE - ▁STRUCTURE - ▁SKETCH - ▁ULTIMATE - ▁GLOBE - ▁INSECT - FICIENCY - ▁ORCHARD - ▁AMIABLE - ▁DESCENT - ▁INDEPENDENCE - ▁MANUFACTURE - ▁SPRINKLE - ▁NIGHTINGALE - ▁CUSHION - ▁EMINENT - ▁SCOTT - ▁ARRAY - ▁COSETTE - ▁WAVING - ▁EXTRACT - ▁IRREGULAR - ▁PERSECUT - ▁DERIVED - ▁WITHDREW - ▁CAUTION - ▁SUSPICIOUS - ▁MEMORIES - ▁NOWHERE - ▁SUBTLE - ▁THOROUGH - Q - ▁APPROPRIATE - ▁SLAUGHTER - ▁YOURSELVES - ▁THUMB - ▁TWAS - ▁ABODE - ▁BIDDING - ▁CONSPICUOUS - ▁REBECCA - ▁SERGEANT - ▁APRON - ▁ANTICIPATE - ▁DISCIPLINE - ▁GLANCING - ▁PILGRIM - ▁SULLEN - ▁CONTRIBUTE - ▁PRAIRIE - ▁CARVED - ▁COMMERCE - ▁EXCLAMATION - ▁MUSCULAR - ▁NOVEMBER - ▁PHENOMENA - ▁SYMBOL - ▁UMBRELLA - ▁DIMINISH - ▁PARLOUR - ▁THREATENING - ▁STUMP - ▁EXTENSIVE - ▁PLEASING - ▁REMEMBRANCE - ▁COMBINED - ▁SHERIFF - ▁SHAFT - ▁LAURA - ▁INTERCOURSE - ▁STRICKEN - ▁SUPPLIES - ▁LANDLORD - ▁SHRINK - ▁PRICK - ▁CAESAR - ▁DRUG - ▁BEWILDERED - ▁NAUTILUS - ▁BRUTAL - ▁COMMERCIAL - ▁MAGGIE - ▁SPHERE - ▁VIRGIN - ▁BRETHREN - ▁DESTINY - ▁POLICY - ▁TERRIFIED - ▁HOUSEKEEPER - ▁CRAZY - ▁ARDENT - ▁DISCERN - ▁WRAP - ▁MARQUIS - ▁RUSSIA - MOUTH - ▁BRITAIN - ▁HARBOUR - ▁CONCERT - ▁DONKEY - ▁DAMAGE - ▁SLIM - ABOUT - ▁LUXURY - ▁MONSTROUS - ▁TENDENCY - ▁PARADISE - ▁CULTURE - ▁JULIUS - ▁RAOUL - ▁REMEDY - ▁DECAY - ▁SCOLD - ▁SPLIT - ▁ASSAULT - ▁DECEMBER - ▁MOSCOW - ▁EXPLORE - ▁TROUSERS - ▁WRIST - PIECE - ▁MUSKET - ▁VALENTINE - ▁TYRANT - ▁ABRAHAM - ▁MEDIUM - ▁ARTIFICIAL - ▁FACULTY - ▁OBLIGATION - ▁RESEMBLANCE - ▁INQUIRIES - ▁DETAIN - ▁SWARM - ▁PLEDGE - ▁ADMIRABLE - ▁DEFECT - ▁SUPERINTEND - ▁PATRIOT - ▁CLUNG - ▁DISMAL - ▁RECIT - ▁IGNOR - ▁AMELIA - ▁JUSTIFY - ▁ELEPHANT - ▁ESTIMATE - ▁KNELT - ▁SERVING - ▁WHIM - ▁SHRILL - ▁STUDIO - ▁TEXT - ▁ALEXANDER - ▁WROUGHT - ▁ABUNDANT - ▁SITUATED - ▁REGAIN - ▁FIERY - ▁SNEER - ▁SWEAT - ▁GLARE - ▁NIGH - ▁ESCORT - ▁INEVITABLE - ▁PSMITH - ▁RELUCTANT - ▁PRECEDING - ▁RESORT - ▁OUTRAGE - ▁AMBASSADOR - ▁CONSOLATION - ▁RECOGNITION - ▁REMORSE - ▁BEHALF - ▁FORMIDABLE - ▁GRAVITY - ▁DIVIDE - ▁CONFRONT - ▁GIGANTIC - ▁OCTOBER - ▁FLANK - ▁SLEW - ▁CLARA - ▁FILM - ▁BULK - ▁POMP - ▁ELEANOR - ▁EMPHASIS - ▁JAPANESE - ▁CAVALRY - ▁EXCLUSIVE - ▁PERFUME - ▁BRONZE - ▁FEDERAL - ▁LIQUID - ▁RUBBING - ▁OVEN - DOLPH - ▁CONVULS - ▁DEPRIVED - ▁RESPONSIBILITY - ▁SIGNIFICANT - ▁WAISTCOAT - ▁CLUSTER - ▁MARTHA - ▁REVERSE - ▁ATTORNEY - ▁DROOP - ▁SKILFUL - ▁HABITUAL - ▁PUMP - ▁INTERVEN - ▁OWL - ▁CONJECTURE - ▁FANTASTIC - ▁RESPONSIBLE - ▁DESTINED - ▁DOCUMENT - ▁THEREUPON - ▁GODDESS - ▁PACIFIC - ▁WARRANT - ▁COSTUME - ▁BRIDLE - ▁CALIFORNIA - ▁DEMOCRATIC - ▁EUSTACE - ▁SQUIRREL - ▁UNCOMMON - ▁MARVELLOUS - ▁PLOUGH - ▁TRAGEDY - ▁VAULT - ▁HESITATE - ▁REFRAIN - ▁ADMIRING - ▁CORPORAL - ▁ENTITLED - ▁SHREWD - ▁SQUEEZ - ▁ACCURATE - ▁TEMPEST - ▁MONUMENT - ▁SIEGE - ▁CHINESE - ▁RAVEN - ▁LOUNG - ▁ASSASSIN - ▁INFLICT - ▁AGITATED - ▁DESIRABLE - ▁EARLIEST - ▁LAUNCH - ▁PILOT - ▁PULSE - ▁MUTE - LEIGH - ▁LIQUOR - ▁SCARECROW - ▁SKULL - ▁DESOLATE - ▁SUBLIME - ▁SERENE - ▁RECESS - ▁WAKING - ▁CHARLOTTE - ▁CIRCULAR - ▁INJUSTICE - ▁PINOCCHIO - ▁PRISCILLA - ▁THYSELF - ▁OCCURRENCE - ▁CASUAL - ▁FRANTIC - ▁LEGEND - ▁FERTIL - ▁BACKGROUND - ▁DELICACY - ▁ESTRALLA - ▁MANUSCRIPT - ▁RESPONSE - ▁UNIVERSITY - ▁WOLVES - ▁SCANDAL - ▁STUMBLE - ▁HOARSE - ▁BODILY - ▁CONVENT - ▁EXAMINING - ▁INCAPABLE - ▁PERCEIVING - ▁PHILADELPHIA - ▁SUBSEQUENT - ▁THIEVES - ▁ACCUMULAT - ▁DAMSEL - ▁SCOTCH - ▁UNDERNEATH - ▁NOBILITY - ▁SMASH - ▁REVOLT - ▁ENGAGE - ▁CATHEDRAL - ▁CHAMPION - ▁DESPATCH - ▁ETERNITY - ▁JANUARY - ▁PLEADED - ▁PROBABILITY - ▁JIMMIE - ▁PARALLEL - ▁FISHERMAN - ▁JERRY - ▁SWORE - ▁DRAUGHT - ▁OPPONENT - ▁PRIMITIVE - ▁SIGNIFICANCE - ▁SUBSTANTIAL - ▁AMAZED - ▁DUNBAR - ▁COMMEND - ▁CONTEMPLATE - ▁TESTIMONY - ▁IMPERIAL - ▁ADAPT - ▁JUICE - ▁CALAMIT - CULAR - ▁CHATEAU - ▁PHOENIX - ▁PRUDENT - ▁SOLUTION - ▁VILLEFORT - ▁REACTION - ▁RELAX - ▁YU - ▁PROHIBIT - ▁DISTRUST - ▁PLUNDER - ▁WELFARE - ▁NAVIGAT - ▁PARLOR - ▁LAZY - ▁DETACH - OMETER - ▁PRIV - ▁DISCOURAGE - ▁OBSTINATE - ▁REJOICING - ▁SERMON - ▁VEHICLE - ▁FANCIES - ▁ENLIGHTEN - ▁ACUTE - ▁ILLUSION - ▁ANTHEA - ▁MARTIAN - ▁EXCITE - ▁GENEROSITY - OLOGIST - ▁AMAZING - ▁UNWORTHY - ▁INTERNAL - ▁INCENSE - ▁VIBRAT - ▁ADHERE - ROACH - ▁FEBRUARY - ▁MEXICAN - ▁POTATOES - ▁INCESSANT - ▁INTERPOSED - ▁PARCEL - ▁VEXED - ▁PROMOTE - MIDST - ▁ARISTOCRAT - ▁CYRIL - ▁EMBARK - ▁ABUNDANCE - ▁LITERALLY - ▁SURGEON - ▁TERRACE - ▁ATLANTIC - ▁MARTYR - ▁SPECK - ▁SENATE - ▁LOAF - ▁ADMINISTER - ▁APPREHEND - ▁SUBDUED - ▁TEMPORARY - ▁DOMINION - ▁ELABORATE - ▁DIGNIFIED - ▁ELIZA - ▁SPLASH - ▁CONSEIL - ▁DEXTER - ▁UNSEEN - ▁TRAGIC - VOCATION - ▁GRATIFY - ▁BACHELOR - ▁DEFENSE - ▁EXCURSION - ▁FACULTIES - ▁PROPRIETOR - ▁SYMPATHETIC - ▁UNNECESSARY - ▁RADIANT - ▁VACANT - ▁OUNCE - ▁SCREW - ▁PHENOMENON - ▁PROMINENT - ▁WORRIED - ▁STUDIES - ▁CLIMATE - ▁KEITH - ▁ARAMIS - ▁BLISS - ▁CONTINUAL - ▁SURPASS - ▁HEBREW - ▁IDENTITY - ▁PROVOKE - ▁TEMPERAMENT - ▁CHARIOT - ▁HARBOR - ▁NINTH - ▁PRIOR - ▁DESIROUS - ▁JERUSALEM - ▁UNDERTAKING - ▁EDISON - ▁MIRTH - ▁SCOUT - ▁APPARATUS - ▁ILLUSTRATION - ▁INTELLIGIBLE - ▁INVARIABLY - ▁PIERCED - ▁REVIEW - ▁FLICKER - ▁HAZARD - ▁REVELATION - ▁DIXON - ▁EXCITING - ▁GOSPEL - ▁CONSTANCE - ▁OVERTAKE - ▁GUINEA - ▁ALADDIN - ▁CHICAGO - ▁TULLIVER - ▁HAMILTON - ▁GARRISON - ▁DISCIPLE - ▁INTENSITY - ▁TRAITOR - ▁CHANCELLOR - ▁PROVERB - ▁DAGGER - ▁FORESEE - ▁CONFIDE - ▁GLIMMER - ▁CHAUVELIN - ▁ILLUSTRATE - ▁VOLUNTEER - ▁JUNGLE - ▁STREAK - ▁SUNRISE - ▁DISSOLV - ▁QUEST - ▁AWHILE - ▁FELICITY - ▁LEGISLATURE - ▁LEONORA - ▁MAGAZINE - ▁PITIFUL - ▁COLONY - ▁SHAWL - ▁ARRIVING - ▁FUNDAMENTAL - ▁CARPENTER - ▁OVERFLOW - ▁EXPAND - ▁HARVEST - ▁FEMININE - ▁INNUMERABLE - ▁SCRAMBLE - ▁TWENTIETH - ▁TRIFLING - ▁GHASTL - ▁CONQUEST - ▁DANIEL - ▁FACILIT - ▁FORSAKE - ▁BEHAVIOUR - ▁GORGEOUS - ▁PRODUCING - ▁HAPPIER - ▁PROMISING - ▁RAINBOW - ▁INSTINCTIVELY - ▁DECREE - ▁EYEBROWS - ▁IRRESISTIBLE - ▁PHARAOH - ▁SCROOGE - ▁UNNATURAL - ▁CRUMBS - ▁REFINED - ▁DREARY - ▁TRENCH - ▁CONVINCE - ▁FRINGE - ▁EXTREMITY - ▁INTIMACY - ▁SCOUNDREL - ▁SUFFRAGE - ▁UNEASINESS - ▁BARRICADE - ▁CIRCULAT - ▁SAMUEL - ▁BRUCE - ▁DARCY - <sos/eos> input_size: null init: null model_conf: transducer_weight: 1.0 auxiliary_ctc_weight: 0.3 report_cer: true report_wer: true encoder_conf: main_conf: pos_wise_layer_type: linear pos_wise_act_type: swish pos_enc_layer_type: rel_pos conv_mod_act_type: swish input_conf: block_type: conv2d dropout_rate_pos_enc: 0.1 dim_output: 512 dim_conv: 512 body_conf: - block_type: conformer dim_linear: 2048 dim_hidden: 512 heads: 8 dropout_rate: 0.1 dropout_rate_pos_enc: 0.1 dropout_rate_pos_wise: 0.1 dropout_rate_att: 0.1 normalize_before: true macaron_style: true conv_mod_kernel: 31 num_blocks: 12 joint_network_conf: dim_joint_space: 640 use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram5000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz decoder: rnn decoder_conf: rnn_type: lstm num_layers: 1 dim_embedding: 512 dim_hidden: 512 dropout: 0.1 dropout_embed: 0.2 required: - output_dir - token_list version: '202204' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

234c0d2c43a155e73218dc1afe5f9aae

edvinkxs/finetuning-sentiment-model-3000-samples

edvinkxs

distilbert

24

12

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,055

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3498 - Accuracy: 0.8867 - F1: 0.8903 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

73a959fae90930d23c3f21df18e1a1ba

mwrob/distilbert-base-uncased-sexist

mwrob

distilbert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

938

false

# distilbert-base-uncased-sexist This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.11.0

3a8091b123e3d6e7d15715794d06ff6c

ekojs/satdata-sentiment-tuned

ekojs

roberta

11

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,493

false

# satdata-sentiment-tuned This model is a fine-tuned version of [w11wo/indonesian-roberta-base-sentiment-classifier](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2700 - F1: 0.9310 ## 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: 1e-06 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 38 | 0.2717 | 0.9273 | | No log | 2.0 | 76 | 0.2709 | 0.9273 | | No log | 3.0 | 114 | 0.2704 | 0.9310 | | No log | 4.0 | 152 | 0.2701 | 0.9310 | | No log | 5.0 | 190 | 0.2700 | 0.9310 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

26e95ca2b8adb98270ed885f89aecee7

google/ul2

google

t5

14

1,663

transformers

86

text2text-generation

true

false

apache-2.0

['en']

['c4']

null

2

1

0

6

3

[]

false

true

11,874

false

# Introduction UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), apre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. ![model image](https://raw.githubusercontent.com/google-research/google-research/master/ul2/figs/ul2.png) **Abstract** Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives -- two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and find that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups. Finally, by scaling our model up to 20B parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classification, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization. For more information, please take a look at the original paper. Paper: [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) Authors: *Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler* # Training The checkpoint was iteratively pre-trained on C4 and fine-tuned on a variety of datasets ## PreTraining The model is pretrained on the C4 corpus. For pretraining, the model is trained on a total of 1 trillion tokens on C4 (2 million steps) with a batch size of 1024. The sequence length is set to 512/512 for inputs and targets. Dropout is set to 0 during pretraining. Pre-training took slightly more than one month for about 1 trillion tokens. The model has 32 encoder layers and 32 decoder layers, `dmodel` of 4096 and `df` of 16384. The dimension of each head is 256 for a total of 16 heads. Our model uses a model parallelism of 8. The same same sentencepiece tokenizer as T5 of vocab size 32000 is used (click [here](https://huggingface.co/docs/transformers/v4.20.0/en/model_doc/t5#transformers.T5Tokenizer) for more information about the T5 tokenizer). UL-20B can be interpreted as a model that is quite similar to T5 but trained with a different objective and slightly different scaling knobs. UL-20B was trained using the [Jax](https://github.com/google/jax) and [T5X](https://github.com/google-research/t5x) infrastructure. The training objective during pretraining is a mixture of different denoising strategies that are explained in the following: ## Mixture of Denoisers To quote the paper: > We conjecture that a strong universal model has to be exposed to solving diverse set of problems > during pre-training. Given that pre-training is done using self-supervision, we argue that such diversity > should be injected to the objective of the model, otherwise the model might suffer from lack a certain > ability, like long-coherent text generation. > Motivated by this, as well as current class of objective functions, we define three main paradigms that > are used during pre-training: - **R-Denoiser**: The regular denoising is the standard span corruption introduced in [T5](https://huggingface.co/docs/transformers/v4.20.0/en/model_doc/t5) that uses a range of 2 to 5 tokens as the span length, which masks about 15% of input tokens. These spans are short and potentially useful to acquire knowledge instead of learning to generate fluent text. - **S-Denoiser**: A specific case of denoising where we observe a strict sequential order when framing the inputs-to-targets task, i.e., prefix language modeling. To do so, we simply partition the input sequence into two sub-sequences of tokens as context and target such that the targets do not rely on future information. This is unlike standard span corruption where there could be a target token with earlier position than a context token. Note that similar to the Prefix-LM setup, the context (prefix) retains a bidirectional receptive field. We note that S-Denoising with very short memory or no memory is in similar spirit to standard causal language modeling. - **X-Denoiser**: An extreme version of denoising where the model must recover a large part of the input, given a small to moderate part of it. This simulates a situation where a model needs to generate long target from a memory with relatively limited information. To do so, we opt to include examples with aggressive denoising where approximately 50% of the input sequence is masked. This is by increasing the span length and/or corruption rate. We consider a pre-training task to be extreme if it has a long span (e.g., ≥ 12 tokens) or have a large corruption rate (e.g., ≥ 30%). X-denoising is motivated by being an interpolation between regular span corruption and language model like objectives. See the following diagram for a more visual explanation: ![mixture-of-denoisers](https://raw.githubusercontent.com/google-research/google-research/master/ul2/figs/mod.png) **Important**: For more details, please see sections 3.1.2 of the [paper](https://arxiv.org/pdf/2205.05131v1.pdf). ## Fine-tuning The model was continously fine-tuned after N pretraining steps where N is typically from 50k to 100k. In other words, after each Nk steps of pretraining, the model is finetuned on each downstream task. See section 5.2.2 of [paper](https://arxiv.org/pdf/2205.05131v1.pdf) to get an overview of all datasets that were used for fine-tuning). As the model is continuously finetuned, finetuning is stopped on a task once it has reached state-of-the-art to save compute. In total, the model was trained for 2.65 million steps. **Important**: For more details, please see sections 5.2.1 and 5.2.2 of the [paper](https://arxiv.org/pdf/2205.05131v1.pdf). ## Contribution This model was contributed by [Daniel Hesslow](https://huggingface.co/Seledorn). ## Examples The following shows how one can predict masked passages using the different denoising strategies. Given the size of the model the following examples need to be run on at least a 40GB A100 GPU. ### S-Denoising For *S-Denoising*, please make sure to prompt the text with the prefix `[S2S]` as shown below. ```python from transformers import T5ForConditionalGeneration, AutoTokenizer import torch model = T5ForConditionalGeneration.from_pretrained("google/ul2", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("google/ul2") input_string = "[S2S] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man with a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere <extra_id_0>" inputs = tokenizer(input_string, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(inputs, max_length=200) print(tokenizer.decode(outputs[0])) # -> <pad>. Dudley was a very good boy, but he was also very stupid.</s> ``` ### R-Denoising For *R-Denoising*, please make sure to prompt the text with the prefix `[NLU]` as shown below. ```python from transformers import T5ForConditionalGeneration, AutoTokenizer import torch model = T5ForConditionalGeneration.from_pretrained("google/ul2", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("google/ul2") input_string = "[NLU] Mr. Dursley was the director of a firm called <extra_id_0>, which made <extra_id_1>. He was a big, solid man with a bald head. Mrs. Dursley was thin and <extra_id_2> of neck, which came in very useful as she spent so much of her time <extra_id_3>. The Dursleys had a small son called Dudley and <extra_id_4>" inputs = tokenizer(input_string, return_tensors="pt", add_special_tokens=False).input_ids.to("cuda") outputs = model.generate(inputs, max_length=200) print(tokenizer.decode(outputs[0])) # -> "<pad><extra_id_0> Burrows<extra_id_1> brooms for witches and wizards<extra_id_2> had a lot<extra_id_3> scolding Dudley<extra_id_4> a daughter called Petunia. Dudley was a nasty, spoiled little boy who was always getting into trouble. He was very fond of his pet rat, Scabbers.<extra_id_5> Burrows<extra_id_3> screaming at him<extra_id_4> a daughter called Petunia</s> " ``` ### X-Denoising For *X-Denoising*, please make sure to prompt the text with the prefix `[NLG]` as shown below. ```python from transformers import T5ForConditionalGeneration, AutoTokenizer import torch model = T5ForConditionalGeneration.from_pretrained("google/ul2", low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to("cuda") tokenizer = AutoTokenizer.from_pretrained("google/ul2") input_string = "[NLG] Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, solid man wiht a bald head. Mrs. Dursley was thin and blonde and more than the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbours. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere. <extra_id_0>" model.cuda() inputs = tokenizer(input_string, return_tensors="pt", add_special_tokens=False).input_ids.to("cuda") outputs = model.generate(inputs, max_length=200) print(tokenizer.decode(outputs[0])) # -> "<pad><extra_id_0> Burrows<extra_id_1> a lot of money from the manufacture of a product called '' Burrows'''s ''<extra_id_2> had a lot<extra_id_3> looking down people's throats<extra_id_4> a daughter called Petunia. Dudley was a very stupid boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat, ugly boy who was always getting into trouble. He was a big, fat," ```

ddb9e8758a0dd67ddf11f341dcd6be1c

sd-dreambooth-library/true-guweiz-style

sd-dreambooth-library

null

24

3

diffusers

3

text-to-image

false

creativeml-openrail-m

null

3

0

['text-to-image']

false

true

1,886

false

### True-GUWEIZ-Style on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook #### Model by Allenbv This your the Stable Diffusion model fine-tuned the True-GUWEIZ-Style concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)`: **truegwz** You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept: a truegwz paint of {} ![descarga 0](https://huggingface.co/sd-dreambooth-library/true-guweiz-style/resolve/main/concept_images/descarga_(13).png) ![descarga 1](https://huggingface.co/sd-dreambooth-library/true-guweiz-style/resolve/main/concept_images/descarga_(12).png) ![descarga 2](https://huggingface.co/sd-dreambooth-library/true-guweiz-style/resolve/main/concept_images/descarga_(10).png) ![descarga 3](https://huggingface.co/sd-dreambooth-library/true-guweiz-style/resolve/main/concept_images/descarga_(9).png) ![descarga 4](https://huggingface.co/sd-dreambooth-library/true-guweiz-style/resolve/main/concept_images/descarga_(8).png)

3feee417779f2eb2cb8f5ea3178c088a

sd-concepts-library/thegeneral

sd-concepts-library

null

9

0

null

0

null

false

mit

null

0

[]

false

true

1,025

false

### thegeneral on Stable Diffusion This is the `<bobknight>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<bobknight> 0](https://huggingface.co/sd-concepts-library/thegeneral/resolve/main/concept_images/0.jpeg) ![<bobknight> 1](https://huggingface.co/sd-concepts-library/thegeneral/resolve/main/concept_images/3.jpeg) ![<bobknight> 2](https://huggingface.co/sd-concepts-library/thegeneral/resolve/main/concept_images/2.jpeg) ![<bobknight> 3](https://huggingface.co/sd-concepts-library/thegeneral/resolve/main/concept_images/1.jpeg)

ce1a854d5de901707662ab9f0d67475b

Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA

Helsinki-NLP

marian

10

7

transformers

1

translation

true

false

apache-2.0

null

0

['translation']

false

true

1,108

false

### opus-mt-SCANDINAVIA-SCANDINAVIA * source languages: da,fo,is,no,nb,nn,sv * target languages: da,fo,is,no,nb,nn,sv * OPUS readme: [da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/da+fo+is+no+nb+nn+sv-da+fo+is+no+nb+nn+sv/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.da.sv | 69.2 | 0.811 |

deac144913fe1791436d28a56d7514e6

Helsinki-NLP/opus-mt-de-no

Helsinki-NLP

marian

11

128

transformers

0

translation

true

false

apache-2.0

['de', False]

null

0

['translation']

false

true

2,112

false

### deu-nor * source group: German * target group: Norwegian * OPUS readme: [deu-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-nor/README.md) * model: transformer-align * source language(s): deu * target language(s): nno nob * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-nor/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-nor/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-nor/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.deu.nor | 33.2 | 0.554 | ### System Info: - hf_name: deu-nor - source_languages: deu - target_languages: nor - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-nor/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['de', 'no'] - src_constituents: {'deu'} - tgt_constituents: {'nob', 'nno'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-nor/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/deu-nor/opus-2020-06-17.test.txt - src_alpha3: deu - tgt_alpha3: nor - short_pair: de-no - chrF2_score: 0.5539999999999999 - bleu: 33.2 - brevity_penalty: 0.956 - ref_len: 32928.0 - src_name: German - tgt_name: Norwegian - train_date: 2020-06-17 - src_alpha2: de - tgt_alpha2: no - prefer_old: False - long_pair: deu-nor - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

f28ce616dcdb7da47cfc462789ba1a77

google/t5-efficient-base-nh8

google

t5

12

48

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,248

false

# T5-Efficient-BASE-NH8 (Deep-Narrow version) T5-Efficient-BASE-NH8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-base-nh8** - is of model type **Base** with the following variations: - **nh** is **8** It has **194.62** million parameters and thus requires *ca.* **778.48 MB** of memory in full precision (*fp32*) or **389.24 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

5f255325c0ee48ea39cbb382c2ba9377

sd-concepts-library/yoshi

sd-concepts-library

null

10

0

null

1

null

false

mit

null

0

[]

false

true

1,078

false

### Yoshi on Stable Diffusion This is the `<yoshi>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<yoshi> 0](https://huggingface.co/sd-concepts-library/yoshi/resolve/main/concept_images/2.jpeg) ![<yoshi> 1](https://huggingface.co/sd-concepts-library/yoshi/resolve/main/concept_images/3.jpeg) ![<yoshi> 2](https://huggingface.co/sd-concepts-library/yoshi/resolve/main/concept_images/1.jpeg) ![<yoshi> 3](https://huggingface.co/sd-concepts-library/yoshi/resolve/main/concept_images/4.jpeg) ![<yoshi> 4](https://huggingface.co/sd-concepts-library/yoshi/resolve/main/concept_images/0.jpeg)

f89e4f0ef03ead0a2a55093dcaf09252

gabrielsgaspar/bert-base-uncased-emotions-augmented

gabrielsgaspar

bert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,752

false

# bert-base-uncased-emotions-augmented This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9815 - Accuracy: 0.7539 - F1: 0.7506 ## 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: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8475 | 1.0 | 819 | 0.6336 | 0.7655 | 0.7651 | | 0.5594 | 2.0 | 1638 | 0.6109 | 0.7695 | 0.7680 | | 0.4596 | 3.0 | 2457 | 0.6528 | 0.7601 | 0.7556 | | 0.3663 | 4.0 | 3276 | 0.6992 | 0.7631 | 0.7612 | | 0.2809 | 5.0 | 4095 | 0.7773 | 0.7571 | 0.7542 | | 0.2142 | 6.0 | 4914 | 0.8879 | 0.7541 | 0.7504 | | 0.1671 | 7.0 | 5733 | 0.9476 | 0.7552 | 0.7517 | | 0.1416 | 8.0 | 6552 | 0.9815 | 0.7539 | 0.7506 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

73a14b9a56d011bdc16c4d09c3ab7e58