The log likelihood is the log of the likelihood. To get the likelihood from the log likelihood just take the exponential:
$$\text{Likelihood} = e^{\text{Log Likelihood}}$$
This should result in a very small number. Instead you can get the "avg. likelihood" by line in your dataset that is easier to interpret :
$$\text{Avg. Likelihood} = e^{\frac{\text{Log Likelihood}}{\text{Number of Lines}}}$$
Now, what I'm going to say may be true for most basic models, but not for every model.
For a discrete dependent variable $Y$, the likelihood is a probability between 0 and 1. For a continuous dependant variable $Y$, it is the value of the probability density of $Y$ and may not be smaller than 1. This can be interpreted as a probability by units of $Y$.
Anyway, this probability (or density) may not have a very clear meaning. It is not, like in Bayesian analysis, the probability that the parameters are correct. It is the probability that such data is observed given the fitted model.