You can load it with the transformers library like this:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("CohereLabs/rerank-english-v2.0")
text = "Hello World, this is my input string!"
enc = tokenizer(text)
print("Encoded input:")
print(enc)
inv_vocab = {v: k for k, v in tokenizer.vocab.items()}
tokens = [inv_vocab[token_id] for token_id in enc['input_ids']]
print("Tokens:")
print(tokens)
number_of_tokens = len(enc['input_ids'])
print("Number of tokens:", number_of_tokens)
Runs of CohereLabs rerank-english-v2.0 on huggingface.co
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More Information About rerank-english-v2.0 huggingface.co Model
rerank-english-v2.0 huggingface.co
rerank-english-v2.0 huggingface.co is an AI model on huggingface.co that provides rerank-english-v2.0's model effect (), which can be used instantly with this CohereLabs rerank-english-v2.0 model. huggingface.co supports a free trial of the rerank-english-v2.0 model, and also provides paid use of the rerank-english-v2.0. Support call rerank-english-v2.0 model through api, including Node.js, Python, http.
rerank-english-v2.0 huggingface.co is an online trial and call api platform, which integrates rerank-english-v2.0's modeling effects, including api services, and provides a free online trial of rerank-english-v2.0, you can try rerank-english-v2.0 online for free by clicking the link below.
CohereLabs rerank-english-v2.0 online free url in huggingface.co:
rerank-english-v2.0 is an open source model from GitHub that offers a free installation service, and any user can find rerank-english-v2.0 on GitHub to install. At the same time, huggingface.co provides the effect of rerank-english-v2.0 install, users can directly use rerank-english-v2.0 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
rerank-english-v2.0 install url in huggingface.co: