modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 12:31:00
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
andypyc/news_classifier-distilbert-base-uncased-subject-only
|
andypyc
| 2023-07-04T19:44:27Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-04T19:40:56Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: news_classifier-distilbert-base-uncased-subject-only
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# news_classifier-distilbert-base-uncased-subject-only
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: 0.9128
- Accuracy: 0.6719
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 48 | 1.1869 | 0.5417 |
| No log | 2.0 | 96 | 0.9940 | 0.5833 |
| No log | 3.0 | 144 | 0.9497 | 0.5833 |
| No log | 4.0 | 192 | 0.8526 | 0.6146 |
| No log | 5.0 | 240 | 0.8595 | 0.6510 |
| No log | 6.0 | 288 | 0.8548 | 0.6562 |
| No log | 7.0 | 336 | 0.8727 | 0.6823 |
| No log | 8.0 | 384 | 0.9072 | 0.6667 |
| No log | 9.0 | 432 | 0.9282 | 0.6667 |
| No log | 10.0 | 480 | 0.9128 | 0.6719 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jfrojanoj/q-FrozenLake-v1-4x4-noSlippery
|
jfrojanoj
| 2023-07-04T19:38:37Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T19:38:34Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jfrojanoj/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
darkphipps/NinjaAI
|
darkphipps
| 2023-07-04T19:29:56Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"question-answering",
"en",
"dataset:Open-Orca/OpenOrca",
"license:openrail",
"region:us"
] |
question-answering
| 2023-07-04T19:17:39Z |
---
license: openrail
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: adapter-transformers
pipeline_tag: question-answering
---
|
hopkins/eng-ind-wsample.42
|
hopkins
| 2023-07-04T19:18:30Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-04T16:00:01Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-ind-wsample.42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-ind-wsample.42
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7642
- Bleu: 21.7118
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-ind-wsample.49
|
hopkins
| 2023-07-04T19:18:19Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-04T15:59:58Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-ind-wsample.49
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-ind-wsample.49
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7653
- Bleu: 22.0600
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
rohanbalkondekar/adept-skunk
|
rohanbalkondekar
| 2023-07-04T19:06:11Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-04T18:59:30Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="BeRohan/adept-skunk",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"BeRohan/adept-skunk",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"BeRohan/adept-skunk",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "BeRohan/adept-skunk" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BeRohan/adept-skunk --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
Graphcore/mt5-large-ipu
|
Graphcore
| 2023-07-04T19:05:35Z | 0 | 0 | null |
[
"optimum_graphcore",
"arxiv:1910.10683",
"arxiv:2010.11934",
"license:apache-2.0",
"region:us"
] | null | 2023-05-19T15:18:20Z |
---
license: apache-2.0
---
# Graphcore/mt5-large-ipu
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
Multilingual Text-to-Text Transfer Transformer (mT5) is the multilingual variant of [T5](https://arxiv.org/abs/1910.10683). T5 is a Transformer based model that uses a text-to-text approach for translation, question answering, and classification. It introduces an unified framework that converts all text-based language problems into a text-to-text format for transfer learning for NLP. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks.
mT5 is pretrained on the mC4 corpus, covering 101 languages:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
Note: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
Paper link :[mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)
## Intended uses & limitations
This model contains just the `IPUConfig` files for running the mT5 Small model (e.g. [HuggingFace/google/mt5-large](https://huggingface.co/google/mt5-large)) on Graphcore IPUs.
**This model contains no model weights, only an IPUConfig.**
## Usage
```
from optimum.graphcore import IPUConfig
ipu_config = IPUConfig.from_pretrained("Graphcore/mt5-large-ipu")
```
|
aksj/falcon-finetuned-pubmed-lora-r-512
|
aksj
| 2023-07-04T18:47:14Z | 3 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-04T18:40:57Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0.dev0
|
jordyvl/LayoutLMv3_maveriq_tobacco3482_2023-07-04
|
jordyvl
| 2023-07-04T18:35:44Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-04T18:25:14Z |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LayoutLMv3_maveriq_tobacco3482_2023-07-04
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LayoutLMv3_maveriq_tobacco3482_2023-07-04
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9452
- Accuracy: 0.28
## 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: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.96 | 3 | 2.1539 | 0.28 |
| No log | 1.96 | 6 | 2.0282 | 0.275 |
| No log | 2.96 | 9 | 2.0001 | 0.265 |
| No log | 3.96 | 12 | 1.9591 | 0.265 |
| No log | 4.96 | 15 | 1.9452 | 0.28 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
omnitron/LunarLander
|
omnitron
| 2023-07-04T18:34:12Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T18:32:07Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 252.37 +/- 14.32
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hopkins/eng-deu-simcse.dev2.4440
|
hopkins
| 2023-07-04T18:30:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-03T17:07:41Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-simcse.dev2.4440
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-deu-simcse.dev2.4440
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6391
- Bleu: 21.6215
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.42
|
hopkins
| 2023-07-04T18:27:55Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-04T15:59:33Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-deu-wsample.42
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6513
- Bleu: 20.8783
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hopkins/eng-deu-wsample.49
|
hopkins
| 2023-07-04T18:27:49Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-04T15:59:30Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-deu-wsample.49
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-deu-wsample.49
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6500
- Bleu: 21.1322
## 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
wizofavalon/my_awesome_model
|
wizofavalon
| 2023-07-04T18:21:48Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-03T22:07:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.94084
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
- Accuracy: 0.9408
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2142 | 1.0 | 1563 | 0.1712 | 0.9356 |
| 0.1281 | 2.0 | 3126 | 0.2181 | 0.9408 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
RajkNakka/Pixelcopter-PLE-v0
|
RajkNakka
| 2023-07-04T18:04:18Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T17:24:01Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.50 +/- 14.58
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ykirpichev/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
|
ykirpichev
| 2023-07-04T18:01:24Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-04T17:38:06Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.89
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3860
- Accuracy: 0.89
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1996 | 1.0 | 112 | 0.8352 | 0.7 |
| 0.3649 | 2.0 | 225 | 0.4287 | 0.83 |
| 0.2586 | 3.0 | 337 | 0.4005 | 0.86 |
| 0.0021 | 4.0 | 450 | 0.3459 | 0.91 |
| 0.0009 | 4.98 | 560 | 0.3860 | 0.89 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
rohanbalkondekar/QnA-gen
|
rohanbalkondekar
| 2023-07-04T17:54:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"region:us"
] | null | 2023-07-04T17:54:17Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [lmsys/vicuna-7b-v1.3](https://huggingface.co/lmsys/vicuna-7b-v1.3)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.30.1
pip install accelerate==0.20.3
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="BeRohan/QnA-gen",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"BeRohan/QnA-gen",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"BeRohan/QnA-gen",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "BeRohan/QnA-gen" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Model Validation
Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
```bash
CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BeRohan/QnA-gen --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
```
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
Varun1808/CODEGEN-TUNED1
|
Varun1808
| 2023-07-04T17:45:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"codegen",
"text-generation",
"generated_from_trainer",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-04T17:41:12Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
model-index:
- name: CODEGEN-TUNED1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CODEGEN-TUNED1
This model is a fine-tuned version of [Salesforce/codegen-350m-multi](https://huggingface.co/Salesforce/codegen-350m-multi) 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: 1
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 10
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
AyaF/AyaF
|
AyaF
| 2023-07-04T17:43:49Z | 233 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-12-02T10:23:03Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
- Precision
- Recall
- F1Score
model-index:
- name: ArSL VIT
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9934656620025635
- name: Precision
type: Precision
value: 0.9939382672309875
- name: Recall
type: Recall
value: 0.9934656620025635
- name: F1Score
type: F1Score
value: 0.9933341145515442
---
|
SebastianBodza/DElefant
|
SebastianBodza
| 2023-07-04T17:34:56Z | 16 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"de",
"dataset:SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-04T07:59:22Z |
---
license: cc-by-nc-sa-4.0
datasets:
- SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0
language:
- de
---
# DElefant:
<img src="https://huggingface.co/SebastianBodza/DElefant/resolve/main/badge_gerlefant.png" style="max-width:200px">
DElefant is a LLM developed for instruction tuned German interactions. This version is built on top of the adapted BLOOM version from <a href="https://huggingface.co/malteos/bloom-6b4-clp-german">Malte Ostendorff</a> with a opus-mt translated and afterwards filtered <a href="https://huggingface.co/datasets/SebastianBodza/Ger_WizardLM_evol_instruct_70k_V0">WizardLM</a> dataset. The evolved dataset led to SOTA english LLMs and we hope by incoperating the dataset to a german base model we can leverage the capabilities for various tasks including Code generation.
Due to limitation in translation, the comments inside of the code blocks remained english, however the Coding was kept in working condition.
## Model Description:
Full-Finetuning of the German-BLOOM model on an RTX 3090 with the translated WizardLM Dataset.
## Roadmap:
If there is sufficient demand, additional adjustments can be made:
- Native German generated dataset
- Full Fine-Tuning of larger LLMs e.g. Falcon, Starcoderplus, ...
## How to use:
Prompt-Template:
```
{instruction}\n\n### Response:
```
Code example for inference:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("SebastianBodza/DElefant")
model = AutoModelForCausalLM.from_pretrained("SebastianBodza/DElefant", device_map="auto")
frage = "Wie heißt der Bundeskanzler?"
prompt = f"{frage}\n\n### Response:"
txt = tokenizer(prompt, return_tensors="pt").to("cuda")
txt = model.generate(**txt,
max_new_tokens=256,
eos_token_id=tokenizer.eos_token_id)
tokenizer.decode(txt[0], skip_special_tokens=True)
```
## Training:
Training was based on Llama-X with the adaptions of WizardLMs training script.
```
deepspeed Llama-X/src/train_freeform.py \
--model_name_or_path malteos/bloom-6b4-clp-german \
--data_path ger_alpaca_evol_instruct_70k_e.json \
--output_dir ./full_finetune \
--num_train_epochs 2 \
--model_max_length 2048 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 400 \
--save_total_limit 3 \
--learning_rate 2e-5 \
--warmup_steps 2 \
--logging_steps 2 \
--lr_scheduler_type "cosine" \
--report_to "tensorboard" \
--gradient_checkpointing True \
--deepspeed deepspeed.json \
--bf16 True
```
<img src="https://huggingface.co/SebastianBodza/DElefant/resolve/main/train_loss_DElefant.svg" style="max-width:350px">
|
ykirpichev/distilhubert-finetuned-gtzan-finetuned-gtzan
|
ykirpichev
| 2023-07-04T17:33:02Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-04T15:29:02Z |
---
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.83
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan-finetuned-gtzan
This model is a fine-tuned version of [ykirpichev/distilhubert-finetuned-gtzan](https://huggingface.co/ykirpichev/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0892
- Accuracy: 0.83
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0171 | 1.0 | 113 | 1.8133 | 0.73 |
| 0.0637 | 2.0 | 226 | 1.3377 | 0.79 |
| 0.2052 | 3.0 | 339 | 0.8646 | 0.88 |
| 0.0019 | 4.0 | 452 | 1.0868 | 0.82 |
| 0.0003 | 5.0 | 565 | 1.0892 | 0.83 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
digiplay/ShampooMix_4
|
digiplay
| 2023-07-04T17:28:03Z | 297 | 6 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-20T08:34:34Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
https://civitai.com/models/33918/shampoo-mix
|
darthPanda/ppo-LunarLander-v1
|
darthPanda
| 2023-07-04T17:23:17Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T17:17:13Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 274.63 +/- 21.45
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
AIRI-Institute/gena-lm-bigbird-base-sparse
|
AIRI-Institute
| 2023-07-04T17:20:34Z | 49 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"pretraining",
"dna",
"human_genome",
"custom_code",
"arxiv:2002.04745",
"endpoints_compatible",
"region:us"
] | null | 2023-04-02T14:30:00Z |
---
tags:
- dna
- human_genome
---
# GENA-LM (gena-lm-bigbird-base-sparse)
GENA-LM is a Family of Open-Source Foundational Models for Long DNA Sequences.
GENA-LM models are transformer masked language models trained on human DNA sequence.
`gena-lm-bigbird-base-sparse` follows the BigBird architecture and uses sparse attention from DeepSpeed.
Differences between GENA-LM (`gena-lm-bigbird-base-sparse`) and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 36000 nucleotides (4096 BPE tokens) compared to 512 nucleotides of DNABERT;
- pre-training on T2T vs. GRCh38.p13 human genome assembly.
Source code and data: https://github.com/AIRI-Institute/GENA_LM
Paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
## Installation
`gena-lm-bigbird-base-sparse` sparse ops require DeepSpeed.
### DeepSpeed
DeepSpeed installation is needed to work with SparseAttention versions of language models. DeepSpeed Sparse attention supports only GPUs with compute compatibility >= 7 (V100, T4, A100).
```bash
pip install triton==1.0.0
DS_BUILD_SPARSE_ATTN=1 pip install deepspeed==0.6.0 --global-option="build_ext" --global-option="-j8" --no-cache
```
and check installation with
```bash
ds_report
```
### APEX for FP16
Install APEX https://github.com/NVIDIA/apex#quick-start
```
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
```
## Examples
### How to load pre-trained model for Masked Language Modeling
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', trust_remote_code=True)
```
### How to load pre-trained model to fine-tune it on classification task
Get model class from GENA-LM repository:
```bash
git clone https://github.com/AIRI-Institute/GENA_LM.git
```
```python
from GENA_LM.src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse')
```
or you can just download [modeling_bert.py](https://github.com/AIRI-Institute/GENA_LM/tree/main/src/gena_lm) and put it close to your code.
OR you can get model class from HuggingFace AutoModel:
```python
from transformers import AutoTokenizer, AutoModel
model = AutoModel.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', trust_remote_code=True)
gena_module_name = model.__class__.__module__
print(gena_module_name)
import importlib
# available class names:
# - BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
# - BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
# - BertForQuestionAnswering
# check https://huggingface.co/docs/transformers/model_doc/bert
cls = getattr(importlib.import_module(gena_module_name), 'BertForSequenceClassification')
print(cls)
model = cls.from_pretrained('AIRI-Institute/gena-lm-bigbird-base-sparse', num_labels=2)
```
## Model description
GENA-LM (`gena-lm-bigbird-base-sparse`) model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 15% of tokens. Model config for `gena-lm-bigbird-base-sparse` is similar to the `google/bigbird-roberta-base`:
- 4096 Maximum sequence length
- 12 Layers, 12 Attention heads
- 768 Hidden size
- sparse config:
- block size: 64
- random blocks: 3
- global blocks: 2
- sliding window blocks: 3
- Rotary positional embeddings
- 32k Vocabulary size, tokenizer trained on DNA data.
We pre-trained `gena-lm-bigbird-base-sparse` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 810,000 iterations with batch size 256. We modified Transformer with [Pre-Layer normalization](https://arxiv.org/abs/2002.04745).
## Evaluation
For evaluation results, see our paper: https://www.biorxiv.org/content/10.1101/2023.06.12.544594v1
## Citation
```bibtex
@article{GENA_LM,
author = {Veniamin Fishman and Yuri Kuratov and Maxim Petrov and Aleksei Shmelev and Denis Shepelin and Nikolay Chekanov and Olga Kardymon and Mikhail Burtsev},
title = {GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences},
elocation-id = {2023.06.12.544594},
year = {2023},
doi = {10.1101/2023.06.12.544594},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594},
eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.12.544594.full.pdf},
journal = {bioRxiv}
}
```
|
RajkNakka/Reinforce-CartPole-v1
|
RajkNakka
| 2023-07-04T17:14:28Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T17:14:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 474.90 +/- 23.77
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Varun1808/CODEGEN-TUNED
|
Varun1808
| 2023-07-04T16:59:59Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"codegen",
"text-generation",
"generated_from_trainer",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-04T07:39:09Z |
---
license: bsd-3-clause
tags:
- generated_from_trainer
model-index:
- name: CODEGEN-TUNED
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CODEGEN-TUNED
This model is a fine-tuned version of [Salesforce/codegen-350m-multi](https://huggingface.co/Salesforce/codegen-350m-multi) 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: 1
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 10
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jhaddadin/my_awesome_billsum_model
|
jhaddadin
| 2023-07-04T16:43:45Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-04T16:33:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1902
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 2.4141
- Rouge1: 0.1902
- Rouge2: 0.0883
- Rougel: 0.1607
- Rougelsum: 0.1605
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.4766 | 0.1571 | 0.0575 | 0.1311 | 0.1309 | 19.0 |
| No log | 2.0 | 124 | 2.4382 | 0.188 | 0.085 | 0.1577 | 0.1576 | 19.0 |
| No log | 3.0 | 186 | 2.4194 | 0.1911 | 0.089 | 0.1612 | 0.161 | 19.0 |
| No log | 4.0 | 248 | 2.4141 | 0.1902 | 0.0883 | 0.1607 | 0.1605 | 19.0 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Babaili/videomae-base-finetuned-ucf101-subset
|
Babaili
| 2023-07-04T16:35:36Z | 59 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"videomae",
"video-classification",
"generated_from_trainer",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-06-21T08:51:24Z |
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-ucf101-subset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3724
- Accuracy: 0.8387
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 148
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5571 | 0.26 | 38 | 1.2529 | 0.5429 |
| 0.5959 | 1.26 | 76 | 0.5709 | 0.7857 |
| 0.3211 | 2.26 | 114 | 0.4260 | 0.8143 |
| 0.2013 | 3.23 | 148 | 0.3246 | 0.9 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ceefax/distilhubert-finetuned-gtzan
|
ceefax
| 2023-07-04T16:34:55Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-07-04T14:58:10Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5778
- Accuracy: 0.81
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7683 | 1.0 | 113 | 1.8297 | 0.53 |
| 1.175 | 2.0 | 226 | 1.2060 | 0.67 |
| 0.9578 | 3.0 | 339 | 0.9063 | 0.72 |
| 0.5966 | 4.0 | 452 | 0.7675 | 0.76 |
| 0.461 | 5.0 | 565 | 0.6908 | 0.77 |
| 0.2916 | 6.0 | 678 | 0.5942 | 0.85 |
| 0.2538 | 7.0 | 791 | 0.6129 | 0.82 |
| 0.3156 | 8.0 | 904 | 0.5881 | 0.82 |
| 0.2019 | 9.0 | 1017 | 0.5949 | 0.81 |
| 0.1736 | 10.0 | 1130 | 0.5778 | 0.81 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
mamachang/whisper
|
mamachang
| 2023-07-04T16:22:51Z | 7 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-05-22T21:39:43Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
breadlicker45/musenet-untrained
|
breadlicker45
| 2023-07-04T16:20:42Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-28T21:08:49Z |
this is untrained meaning it will not do ANYTHING, DO NOT DOWNLOAD UNLESS YOU ARE GOING TO TRAIN IT.
|
breadlicker45/neox-musenet-untrained
|
breadlicker45
| 2023-07-04T16:20:29Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-06-29T16:54:09Z |
this is untrained meaning it will not do ANYTHING, DO NOT DOWNLOAD UNLESS YOU ARE GOING TO TRAIN IT.
|
khalidalt/m2m100_418M-finetuned-en-to-ar
|
khalidalt
| 2023-07-04T16:18:34Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"en",
"ar",
"dataset:opus100",
"dataset:un_multi",
"arxiv:2010.11125",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-04T13:06:30Z |
---
license: mit
datasets:
- opus100
- un_multi
language:
- en
- ar
---
M2M100 418M
M2M100 is a multilingual encoder-decoder transformer model trained for Many-to-Many multilingual translation. The model, originally introduced by researchers at Facebook, demonstrates impressive performance in cross-lingual translation tasks.
For a better understanding of M2M100 you can look into the [paper](https://arxiv.org/abs/2010.11125) and the associated [repository](https://github.com/facebookresearch/fairseq/tree/main/examples/m2m_100).
To further enhance the capabilities of M2M100, we conducted finetuning experiments on English-to-Arabic parallel text. The finetuning process involved training the model for 1000K steps using a batch size of 8.
|
LarryAIDraw/fgoYangguifeiv1
|
LarryAIDraw
| 2023-07-04T16:18:21Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-04T15:43:05Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/102725/yangguifei-4-outfits-fate-grand-order-4-riuki-lora
|
LarryAIDraw/Cecily_v1.0
|
LarryAIDraw
| 2023-07-04T16:17:33Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-04T16:01:51Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/100322?modelVersionId=107380
|
velascoluis/falcon7b-instruct-database-ft-50-epochs
|
velascoluis
| 2023-07-04T16:12:11Z | 0 | 0 | null |
[
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2023-07-04T16:11:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: falcon7b-instruct-database-ft-50-epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# falcon7b-instruct-database-ft-50-epochs
This model is a fine-tuned version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5833
## 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: 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: 50
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Shresthadev403/codeparrot-ds
|
Shresthadev403
| 2023-07-04T15:58:39Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-04T14:25:16Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dp15/cartProb
|
dp15
| 2023-07-04T15:52:29Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-04T15:52:10Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartProb
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
TheBloke/WizardLM-13B-V1.0-Uncensored-GGML
|
TheBloke
| 2023-07-04T15:51:20Z | 0 | 19 | null |
[
"en",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:other",
"region:us"
] | null | 2023-06-20T07:13:45Z |
---
inference: false
license: other
datasets:
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Eric Hartford's WizardLM-13b-V1.0-Uncensored GGML
These files are GGML format model files for [Eric Hartford's WizardLM-13b-V1.0-Uncensored](https://huggingface.co/ehartford/WizardLM-13b-V1.0-Uncensored).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.0-Uncensored-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.0-Uncensored-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ehartford/WizardLM-13b-V1.0-Uncensored)
## Prompt template
```
You are a helpful AI assistant.
USER: <prompt>
ASSISTANT:
```
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
These are guaranteed to be compatbile with any UIs, tools and libraries released since late May.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt.
## Explanation of the new k-quant methods
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB | 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB | 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB | 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB | 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB | 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB | 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB | 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB | 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| wizardlm-13b-v1.0-uncensored.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m wizardlm-13b-v1.0-uncensored.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
If you're able to use full GPU offloading, you should use `-t 1` to get best performance.
If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Mano Prime, Fen Risland, Derek Yates, Preetika Verma, webtim, Sean Connelly, Alps Aficionado, Karl Bernard, Junyu Yang, Nathan LeClaire, Chris McCloskey, Lone Striker, Asp the Wyvern, Eugene Pentland, Imad Khwaja, trip7s trip, WelcomeToTheClub, John Detwiler, Artur Olbinski, Khalefa Al-Ahmad, Trenton Dambrowitz, Talal Aujan, Kevin Schuppel, Luke Pendergrass, Pyrater, Joseph William Delisle, terasurfer , vamX, Gabriel Puliatti, David Flickinger, Jonathan Leane, Iucharbius , Luke, Deep Realms, Cory Kujawski, ya boyyy, Illia Dulskyi, senxiiz, Johann-Peter Hartmann, John Villwock, K, Ghost , Spiking Neurons AB, Nikolai Manek, Rainer Wilmers, Pierre Kircher, biorpg, Space Cruiser, Ai Maven, subjectnull, Willem Michiel, Ajan Kanaga, Kalila, chris gileta, Oscar Rangel.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Eric Hartford's WizardLM-13b-V1.0-Uncensored
This is a retraining of https://huggingface.co/WizardLM/WizardLM-13B-V1.0 with a filtered dataset, intended to reduce refusals, avoidance, and bias.
Note that LLaMA itself has inherent ethical beliefs, so there's no such thing as a "truly uncensored" model. But this model will be more compliant than WizardLM/WizardLM-7B-V1.0.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
Like WizardLM/WizardLM-13B-V1.0, this model is trained with Vicuna-1.1 style prompts.
```
You are a helpful AI assistant.
USER: <prompt>
ASSISTANT:
```
Thank you [chirper.ai](https://chirper.ai) for sponsoring some of my compute!
|
Word2vec/nlpl_113
|
Word2vec
| 2023-07-04T15:33:20Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:23:18Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1487995 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_113", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/113.zip
|
Word2vec/nlpl_112
|
Word2vec
| 2023-07-04T15:32:12Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:22:27Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2551820 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_112", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/112.zip
|
Word2vec/nlpl_111
|
Word2vec
| 2023-07-04T15:31:59Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:21:44Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2239665 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_111", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/111.zip
|
Word2vec/nlpl_110
|
Word2vec
| 2023-07-04T15:31:40Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:20:00Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 4428648 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_110", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/110.zip
|
wqewq/zhangjingyi
|
wqewq
| 2023-07-04T15:31:40Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-07-01T07:06:48Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Word2vec/nlpl_108
|
Word2vec
| 2023-07-04T15:31:22Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:NBDigital",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:17:51Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NBDigital
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2390583 corresponding to 813922111 tokens from the dataset `NBDigital`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_108", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/108.zip
|
Word2vec/nlpl_106
|
Word2vec
| 2023-07-04T15:30:51Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:NoWaC",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:16:31Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NoWaC
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1356632 corresponding to 687209465 tokens from the dataset `NoWaC`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_106", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/106.zip
|
Word2vec/nlpl_105
|
Word2vec
| 2023-07-04T15:30:39Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:NoWaC",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:16:04Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NoWaC
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1199274 corresponding to 687209465 tokens from the dataset `NoWaC`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_105", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/105.zip
|
Word2vec/nlpl_103
|
Word2vec
| 2023-07-04T15:30:16Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:14:59Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1487994 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_103", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/103.zip
|
Word2vec/nlpl_102
|
Word2vec
| 2023-07-04T15:30:05Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:14:08Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2551819 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_102", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/102.zip
|
Word2vec/nlpl_101
|
Word2vec
| 2023-07-04T15:29:52Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T14:13:23Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2239664 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_101", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/101.zip
|
Word2vec/nlpl_97
|
Word2vec
| 2023-07-04T15:29:02Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:NBDigital",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:49:41Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NBDigital
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2187702 corresponding to 813922111 tokens from the dataset `NBDigital`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_97", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/97.zip
|
Word2vec/nlpl_92
|
Word2vec
| 2023-07-04T15:27:59Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:46:28Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2551819 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_92", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/92.zip
|
Word2vec/nlpl_91
|
Word2vec
| 2023-07-04T15:27:49Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:45:37Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2239664 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Bag-of-Words with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_91", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/91.zip
|
Word2vec/nlpl_89
|
Word2vec
| 2023-07-04T15:27:20Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:NBDigital",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:38:43Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NBDigital
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 2187703 corresponding to 813922111 tokens from the dataset `NBDigital`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_89", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/89.zip
|
Word2vec/nlpl_87
|
Word2vec
| 2023-07-04T15:26:55Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:NoWaC",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:37:31Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: NoWaC
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1199275 corresponding to 687209465 tokens from the dataset `NoWaC`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_87", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/87.zip
|
Word2vec/nlpl_86
|
Word2vec
| 2023-07-04T15:26:47Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:36:21Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1728101 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_86", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/86.zip
|
Word2vec/nlpl_85
|
Word2vec
| 2023-07-04T15:26:25Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:32:33Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@unit.no) on a vocabulary of size 1487995 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 15 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_85", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/85.zip
|
Word2vec/nlpl_81
|
Word2vec
| 2023-07-04T15:25:49Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norsk_Aviskorpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:26:17Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norsk_Aviskorpus
---
## Information
A word2vec model trained by Cathrine Stadsnes (cathrine.stadsnes@usit.uio.no) on a vocabulary of size 4428648 corresponding to 1527414377 tokens from the dataset `Norsk_Aviskorpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_81", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/81.zip
|
Word2vec/nlpl_74
|
Word2vec
| 2023-07-04T15:23:35Z | 0 | 0 | null |
[
"word2vec",
"vie",
"dataset:Vietnamese_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:11:45Z |
---
language: vie
license: cc-by-4.0
tags:
- word2vec
datasets: Vietnamese_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 3847942 corresponding to 4233272187 tokens from the dataset `Vietnamese_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_74", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/74.zip
|
Word2vec/nlpl_70
|
Word2vec
| 2023-07-04T15:22:43Z | 0 | 0 | null |
[
"word2vec",
"tur",
"dataset:Turkish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:51:26Z |
---
language: tur
license: cc-by-4.0
tags:
- word2vec
datasets: Turkish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 3633786 corresponding to 3668140172 tokens from the dataset `Turkish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_70", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/70.zip
|
Word2vec/nlpl_69
|
Word2vec
| 2023-07-04T15:22:31Z | 0 | 0 | null |
[
"word2vec",
"swe",
"dataset:Swedish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:50:25Z |
---
language: swe
license: cc-by-4.0
tags:
- word2vec
datasets: Swedish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 3010472 corresponding to 3101022237 tokens from the dataset `Swedish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_69", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/69.zip
|
Word2vec/nlpl_68
|
Word2vec
| 2023-07-04T15:22:18Z | 0 | 0 | null |
[
"word2vec",
"spa",
"dataset:Spanish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T13:10:25Z |
---
language: spa
license: cc-by-4.0
tags:
- word2vec
datasets: Spanish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 2656057 corresponding to 5967877096 tokens from the dataset `Spanish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_68", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/68.zip
|
Word2vec/nlpl_67
|
Word2vec
| 2023-07-04T15:22:03Z | 0 | 0 | null |
[
"word2vec",
"slv",
"dataset:Slovenian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:50:05Z |
---
language: slv
license: cc-by-4.0
tags:
- word2vec
datasets: Slovenian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 706835 corresponding to 545624885 tokens from the dataset `Slovenian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_67", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/67.zip
|
Word2vec/nlpl_66
|
Word2vec
| 2023-07-04T15:21:53Z | 0 | 0 | null |
[
"word2vec",
"slk",
"dataset:Slovak_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:49:41Z |
---
language: slk
license: cc-by-4.0
tags:
- word2vec
datasets: Slovak_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 1188804 corresponding to 855770850 tokens from the dataset `Slovak_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_66", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/66.zip
|
Word2vec/nlpl_63
|
Word2vec
| 2023-07-04T15:21:02Z | 0 | 0 | null |
[
"word2vec",
"por",
"dataset:Portuguese_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:46:03Z |
---
language: por
license: cc-by-4.0
tags:
- word2vec
datasets: Portuguese_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 2536452 corresponding to 6173041573 tokens from the dataset `Portuguese_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_63", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/63.zip
|
Word2vec/nlpl_62
|
Word2vec
| 2023-07-04T15:20:23Z | 0 | 0 | null |
[
"word2vec",
"pol",
"dataset:Polish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:44:03Z |
---
language: pol
license: cc-by-4.0
tags:
- word2vec
datasets: Polish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 4420598 corresponding to 5489171333 tokens from the dataset `Polish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_62", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/62.zip
|
Word2vec/nlpl_59
|
Word2vec
| 2023-07-04T15:17:45Z | 0 | 0 | null |
[
"word2vec",
"nno",
"dataset:Norwegian-Nynorsk_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:34:41Z |
---
language: nno
license: cc-by-4.0
tags:
- word2vec
datasets: Norwegian-Nynorsk_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 223763 corresponding to 78538310 tokens from the dataset `Norwegian-Nynorsk_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_59", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/59.zip
|
Word2vec/nlpl_58
|
Word2vec
| 2023-07-04T15:17:32Z | 0 | 0 | null |
[
"word2vec",
"nob",
"dataset:Norwegian-Bokmaal_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:33:56Z |
---
language: nob
license: cc-by-4.0
tags:
- word2vec
datasets: Norwegian-Bokmaal_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 1182371 corresponding to 1377663508 tokens from the dataset `Norwegian-Bokmaal_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_58", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/58.zip
|
Word2vec/nlpl_56
|
Word2vec
| 2023-07-04T15:17:20Z | 0 | 0 | null |
[
"word2vec",
"lat",
"dataset:Latin_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:33:18Z |
---
language: lat
license: cc-by-4.0
tags:
- word2vec
datasets: Latin_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 555381 corresponding to 256719661 tokens from the dataset `Latin_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_56", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/56.zip
|
Word2vec/nlpl_53
|
Word2vec
| 2023-07-04T15:15:57Z | 0 | 0 | null |
[
"word2vec",
"jpn",
"dataset:Japanese_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:30:00Z |
---
language: jpn
license: cc-by-4.0
tags:
- word2vec
datasets: Japanese_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 3989605 corresponding to 5458595968 tokens from the dataset `Japanese_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_53", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/53.zip
|
Word2vec/nlpl_51
|
Word2vec
| 2023-07-04T15:15:31Z | 0 | 0 | null |
[
"word2vec",
"gle",
"dataset:Irish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:28:40Z |
---
language: gle
license: cc-by-4.0
tags:
- word2vec
datasets: Irish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 87115 corresponding to 25270102 tokens from the dataset `Irish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_51", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/51.zip
|
Word2vec/nlpl_50
|
Word2vec
| 2023-07-04T15:15:03Z | 0 | 0 | null |
[
"word2vec",
"ind",
"dataset:Indonesian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:27:36Z |
---
language: ind
license: cc-by-4.0
tags:
- word2vec
datasets: Indonesian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 2899107 corresponding to 5455674387 tokens from the dataset `Indonesian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_50", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/50.zip
|
Word2vec/nlpl_46
|
Word2vec
| 2023-07-04T15:14:03Z | 0 | 0 | null |
[
"word2vec",
"ell",
"dataset:Greek_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:19:22Z |
---
language: ell
license: cc-by-4.0
tags:
- word2vec
datasets: Greek_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 1183194 corresponding to 770507143 tokens from the dataset `Greek_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_46", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/46.zip
|
Word2vec/nlpl_44
|
Word2vec
| 2023-07-04T15:13:22Z | 0 | 0 | null |
[
"word2vec",
"glg",
"dataset:Galician_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:17:35Z |
---
language: glg
license: cc-by-4.0
tags:
- word2vec
datasets: Galician_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 363106 corresponding to 272960803 tokens from the dataset `Galician_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_44", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/44.zip
|
davanstrien/convnext_manuscript_iiif
|
davanstrien
| 2023-07-04T15:13:12Z | 253 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-base-224-22k",
"base_model:finetune:facebook/convnext-base-224-22k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- f1
base_model: facebook/convnext-base-224-22k
model-index:
- name: convnext_manuscript_iiif
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext_manuscript_iiif
This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5856
- F1: 0.0037
## 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: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.5753 | 1.0 | 2038 | 6.4121 | 0.0016 |
| 5.9865 | 2.0 | 4076 | 5.9466 | 0.0021 |
| 5.6521 | 3.0 | 6114 | 5.7645 | 0.0029 |
| 5.3123 | 4.0 | 8152 | 5.6890 | 0.0033 |
| 5.0337 | 5.0 | 10190 | 5.6692 | 0.0034 |
| 4.743 | 6.0 | 12228 | 5.5856 | 0.0037 |
| 4.4387 | 7.0 | 14266 | 5.5969 | 0.0042 |
| 4.1422 | 8.0 | 16304 | 5.6711 | 0.0043 |
| 3.8372 | 9.0 | 18342 | 5.6761 | 0.0044 |
| 3.5244 | 10.0 | 20380 | 5.8469 | 0.0042 |
| 3.2321 | 11.0 | 22418 | 5.8774 | 0.0045 |
| 2.9004 | 12.0 | 24456 | 6.1186 | 0.0047 |
| 2.5937 | 13.0 | 26494 | 6.2398 | 0.0046 |
| 2.2983 | 14.0 | 28532 | 6.3732 | 0.0049 |
| 2.0611 | 15.0 | 30570 | 6.5024 | 0.0045 |
| 1.8153 | 16.0 | 32608 | 6.6585 | 0.0047 |
| 1.6075 | 17.0 | 34646 | 6.8333 | 0.0043 |
| 1.4342 | 18.0 | 36684 | 6.9529 | 0.0044 |
| 1.2614 | 19.0 | 38722 | 7.1129 | 0.0046 |
| 1.1463 | 20.0 | 40760 | 7.1977 | 0.0039 |
| 1.0387 | 21.0 | 42798 | 7.2700 | 0.0044 |
| 0.9635 | 22.0 | 44836 | 7.3375 | 0.0040 |
| 0.8872 | 23.0 | 46874 | 7.4003 | 0.0039 |
| 0.8156 | 24.0 | 48912 | 7.4884 | 0.0039 |
| 0.7544 | 25.0 | 50950 | 7.4764 | 0.0039 |
| 0.6893 | 26.0 | 52988 | 7.5153 | 0.0042 |
| 0.6767 | 27.0 | 55026 | 7.5427 | 0.0043 |
| 0.6098 | 28.0 | 57064 | 7.5547 | 0.0042 |
| 0.5871 | 29.0 | 59102 | 7.5533 | 0.0041 |
| 0.5696 | 30.0 | 61140 | 7.5595 | 0.0041 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.6
|
Word2vec/nlpl_42
|
Word2vec
| 2023-07-04T15:12:50Z | 0 | 0 | null |
[
"word2vec",
"fin",
"dataset:Finnish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:16:45Z |
---
language: fin
license: cc-by-4.0
tags:
- word2vec
datasets: Finnish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 2433286 corresponding to 1052546686 tokens from the dataset `Finnish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_42", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/42.zip
|
Word2vec/nlpl_41
|
Word2vec
| 2023-07-04T15:12:33Z | 0 | 0 | null |
[
"word2vec",
"est",
"dataset:Estonian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:16:26Z |
---
language: est
license: cc-by-4.0
tags:
- word2vec
datasets: Estonian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 926795 corresponding to 341986187 tokens from the dataset `Estonian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_41", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/41.zip
|
fawzyhamdy/autotrain-datadata-72110138863
|
fawzyhamdy
| 2023-07-04T15:12:08Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"longt5",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:fawzyhamdy/autotrain-data-datadata",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-07-04T13:57:31Z |
---
tags:
- autotrain
- summarization
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- fawzyhamdy/autotrain-data-datadata
co2_eq_emissions:
emissions: 49.24949877129796
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 72110138863
- CO2 Emissions (in grams): 49.2495
## Validation Metrics
- Loss: 2.501
- Rouge1: 1.345
- Rouge2: 0.000
- RougeL: 1.343
- RougeLsum: 1.365
- Gen Len: 18.982
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/fawzyhamdy/autotrain-datadata-72110138863
```
|
Word2vec/nlpl_40
|
Word2vec
| 2023-07-04T15:12:08Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T12:00:54Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 4027169 corresponding to 9974357994 tokens from the dataset `English_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_40", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/40.zip
|
Word2vec/nlpl_38
|
Word2vec
| 2023-07-04T15:11:38Z | 0 | 0 | null |
[
"word2vec",
"dan",
"dataset:Danish_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T11:59:05Z |
---
language: dan
license: cc-by-4.0
tags:
- word2vec
datasets: Danish_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 1655886 corresponding to 1641664057 tokens from the dataset `Danish_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_38", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/38.zip
|
Word2vec/nlpl_35
|
Word2vec
| 2023-07-04T15:10:53Z | 0 | 0 | null |
[
"word2vec",
"zho",
"dataset:ChineseT_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T11:57:08Z |
---
language: zho
license: cc-by-4.0
tags:
- word2vec
datasets: ChineseT_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 1935503 corresponding to 1608425218 tokens from the dataset `ChineseT_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_35", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/35.zip
|
Graphcore/sentence-t5-large
|
Graphcore
| 2023-07-04T15:10:52Z | 0 | 0 | null |
[
"optimum_graphcore",
"license:apache-2.0",
"region:us"
] | null | 2023-07-04T14:43:53Z |
---
license: apache-2.0
---
# Graphcore/sentence-t5-large
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
(source: https://huggingface.co/sentence-transformers/sentence-t5-large)
Sentence-t5 is a sentence-transformers model, it maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model st5-large-1 to PyTorch. When using this model, have a look at the publication: Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-large model. The weights are stored in FP16.
## Intended uses & limitations
This model contains just the `IPUConfig` files for running the `sentence-t5-large` model (e.g. [sentence-transformers/sentence-t5-large](https://huggingface.co/sentence-transformers/sentence-t5-large)) on Graphcore IPUs.
**This model contains no model weights, only an IPUConfig.**
## Usage
```
from optimum.graphcore import IPUConfig
from transformers import T5EncoderModel
ipu_config = IPUConfig.from_pretrained("Graphcore/sentence-t5-large")
model = T5EncoderModel.from_pretrained("sentence-transformers/sentence-t5-large")
```
|
Word2vec/nlpl_33
|
Word2vec
| 2023-07-04T15:10:24Z | 0 | 0 | null |
[
"word2vec",
"bul",
"dataset:Bulgarian_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T11:56:35Z |
---
language: bul
license: cc-by-4.0
tags:
- word2vec
datasets: Bulgarian_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 628026 corresponding to 388433724 tokens from the dataset `Bulgarian_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_33", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/33.zip
|
Word2vec/nlpl_31
|
Word2vec
| 2023-07-04T15:03:04Z | 0 | 0 | null |
[
"word2vec",
"ara",
"dataset:Arabic_CoNLL17_corpus",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:11:17Z |
---
language: ara
license: cc-by-4.0
tags:
- word2vec
datasets: Arabic_CoNLL17_corpus
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 1071056 corresponding to 1009356735 tokens from the dataset `Arabic_CoNLL17_corpus`.
The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_31", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/31.zip
|
EllaHong/datamap_polyglot_12.8b_exp1_0704
|
EllaHong
| 2023-07-04T15:02:50Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-04T15:02:42Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
Word2vec/nlpl_29
|
Word2vec
| 2023-07-04T15:02:30Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:10:56Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 297790 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 2 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_29", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/29.zip
|
Word2vec/nlpl_28
|
Word2vec
| 2023-07-04T15:02:08Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:10:42Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 209865 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_28", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/28.zip
|
Word2vec/nlpl_27
|
Word2vec
| 2023-07-04T15:01:55Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:10:28Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 209865 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_27", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/27.zip
|
Word2vec/nlpl_26
|
Word2vec
| 2023-07-04T15:01:41Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:10:14Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 209512 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_26", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/26.zip
|
Word2vec/nlpl_25
|
Word2vec
| 2023-07-04T15:01:24Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:10:00Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 228671 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_25", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/25.zip
|
Word2vec/nlpl_24
|
Word2vec
| 2023-07-04T15:01:09Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:09:46Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 228671 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_24", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/24.zip
|
Word2vec/nlpl_23
|
Word2vec
| 2023-07-04T15:00:56Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:09:31Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 228670 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_23", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/23.zip
|
Word2vec/nlpl_19
|
Word2vec
| 2023-07-04T14:58:28Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:08:19Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 260073 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_19", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/19.zip
|
snousias/distilbert-base-uncased-finetuned-imdb
|
snousias
| 2023-07-04T14:57:31Z | 125 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-07-04T14:55:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
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: 2.4742
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7069 | 1.0 | 157 | 2.4947 |
| 2.5792 | 2.0 | 314 | 2.4235 |
| 2.5259 | 3.0 | 471 | 2.4348 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Word2vec/nlpl_13
|
Word2vec
| 2023-07-04T14:56:42Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:06:28Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 262269 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Global Vectors with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_13", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/13.zip
|
Word2vec/nlpl_12
|
Word2vec
| 2023-07-04T14:56:26Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:06:07Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 292479 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: no lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_12", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/12.zip
|
Word2vec/nlpl_11
|
Word2vec
| 2023-07-04T14:56:10Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:Gigaword_5th_Edition",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:05:50Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: Gigaword_5th_Edition
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 261794 corresponding to 4815382730 tokens from the dataset `Gigaword_5th_Edition`.
The model is trained with the following properties: lemmatization and postag with the algorith Gensim Continuous Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_11", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/11.zip
|
Word2vec/nlpl_10
|
Word2vec
| 2023-07-04T14:55:57Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:05:32Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 302815 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: no lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_10", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/10.zip
|
Word2vec/nlpl_9
|
Word2vec
| 2023-07-04T14:55:43Z | 0 | 0 | null |
[
"word2vec",
"eng",
"dataset:English_Wikipedia_Dump_of_February_2017",
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-04T10:05:14Z |
---
language: eng
license: cc-by-4.0
tags:
- word2vec
datasets: English_Wikipedia_Dump_of_February_2017
---
## Information
A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 273930 corresponding to 2252637050 tokens from the dataset `English_Wikipedia_Dump_of_February_2017`.
The model is trained with the following properties: lemmatization and postag with the algorith fastText Skipgram with window of 5 and dimension of 300.
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_9", filename="model.bin"), binary=True, unicode_errors="ignore")
```
## Citation
Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources, In Jörg Tiedemann (ed.), Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017. Linköping University Electronic Press. ISBN 978-91-7685-601-7
This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/9.zip
|
mcamara/ppo-PyramidsRND1
|
mcamara
| 2023-07-04T14:50:48Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-04T14:50:43Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mcamara/ppo-PyramidsRND1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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